Efficient message queuing service using multiplexing

ABSTRACT

Embodiments of the present invention are directed to facilitating efficient message queueing. In particular, embodiments herein describe, among other things, a redelivery monitor used to monitor when to redeliver messages, or tasks, for reprocessing based on expiration of a redelivery deadline. In this regard, markers indicating processing states for tasks being processed are read by the redelivery monitor. When the processing state indicates that processing is ongoing, the redelivery deadline is extended such that a message or task is not redelivered for processing while the message or task is being processed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of Ser. No. 16/592,647 filed Oct. 3,2019 and titled “Efficient Message Queuing Service,” the entire contentsof which are incorporated by reference herein.

BACKGROUND

Modern data centers often include thousands of hosts that operatecollectively to service requests from even larger numbers of remoteclients. During operation, components of these data centers can producesignificant volumes of raw, machine-generated data. Collecting such datais important for performing various types of analyses. For example,collected data, such as machine-generated data (e.g., performance data,diagnostic data, etc.), may be analyzed to diagnose performanceproblems, monitor user interactions, and to derive other insights. Inmany implementations, large-scale data collection is used to collectextensive amounts of data. Such large-scale data collection services canuse queuing services to queue messages, or tasks, for processing (e.g.,to collect data).

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates a networked computer environment in which anembodiment may be implemented;

FIG. 2 illustrates a block diagram of an example data intake and querysystem in which an embodiment may be implemented;

FIG. 3 is a flow diagram that illustrates how indexers process, index,and store data received from forwarders in accordance with the disclosedembodiments;

FIG. 4 is a flow diagram that illustrates how a search head and indexersperform a search query in accordance with the disclosed embodiments;

FIG. 5 illustrates a scenario where a common customer ID is found amonglog data received from three disparate sources in accordance with thedisclosed embodiments;

FIG. 6A illustrates a search screen in accordance with the disclosedembodiments;

FIG. 6B illustrates a data summary dialog that enables a user to selectvarious data sources in accordance with the disclosed embodiments;

FIGS. 7A-7D illustrate a series of user interface screens for an exampledata model-driven report generation interface in accordance with thedisclosed embodiments;

FIG. 8 illustrates an example search query received from a client andexecuted by search peers in accordance with the disclosed embodiments;

FIG. 9A illustrates a key indicators view in accordance with thedisclosed embodiments;

FIG. 9B illustrates an incident review dashboard in accordance with thedisclosed embodiments;

FIG. 9C illustrates a proactive monitoring tree in accordance with thedisclosed embodiments;

FIG. 9D illustrates a user interface screen displaying both log data andperformance data in accordance with the disclosed embodiments;

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system in which an embodiment may be implemented;

FIG. 11 illustrates a block diagram of an example data intake and querysystem that performs searches across external data systems in accordancewith the disclosed embodiments;

FIGS. 12-14 illustrate a series of user interface screens for an exampledata model-driven report generation interface in accordance with thedisclosed embodiments;

FIGS. 15-17 illustrate example visualizations generated by a reportingapplication in accordance with the disclosed embodiments;

FIG. 18 depicts an example data-exchange environment in accordance withvarious embodiments of the present disclosure;

FIG. 19 provides an illustrative data-exchange system, in accordancewith embodiments of the present disclosure;

FIG. 20 illustrates a method for enabling efficient data collection in asecure manner, in accordance with embodiments of the present invention;

FIG. 21 illustrates another method for enabling efficient datacollection in a secure manner, according to embodiments of the presentinvention;

FIG. 22 illustrates another method for enabling efficient datacollection in a secure manner, in accordance with embodiments of thepresent invention;

FIG. 23 depicts an example of a message queueing service, in accordancewith embodiments of the present invention;

FIG. 24A illustrates an example implementation of multiplexing multiplelogical queues to a single topic, in accordance with embodiments of thepresent invention;

FIG. 24B illustrates providing messages associated with multiple logicalqueues to multiple topics;

FIG. 25 depicts an example message queueing service implemented inconnection with a data-exchange service, in accordance with embodimentsof the present invention;

FIG. 26 illustrates a method for providing efficient message queueingservices, in accordance with embodiments of the present invention;

FIG. 27 illustrates a method for providing efficient message queuingservices using a redelivery monitor, in accordance with embodiments ofthe present invention;

FIG. 28 illustrates another method for providing efficient messagequeuing services using a redelivery monitor, in accordance withembodiments of the present invention; and

FIG. 29 is a block diagram of an example computing device in whichembodiments of the present disclosure may be employed.

DETAILED DESCRIPTION

Embodiments are described herein according to the following outline:

1.0. General Overview 2.0. Operating Environment

2.1. Host Devices

2.2. Client Devices

2.3. Client Device Applications

2.4. Data Server System

2.5. Data Ingestion

2.5.1. Input

2.5.2. Parsing

2.5.3. Indexing

2.6. Query Processing

2.7. Field Extraction

2.8. Example Search Screen

2.9. Data Modeling

2.10. Acceleration Techniques

2.10.1. Aggregation Technique

2.10.2. Keyword Index

2.10.3. High Performance Analytics Store

2.10.4. Accelerating Report Generation

2.11. Security Features

2.12. Data Center Monitoring

2.13. Cloud-Based System Overview

2.14. Searching Externally Archived Data

2.14.1. ERP Process Features

2.15 Cloud-Based Architecture

3.0. Overview of Facilitating Scalable and Secure Data Collection

3.1. Scalable Data-Collection System in a Data-Collection Environment

3.2. Scalable and Secure Data Collection Methods

4.0 Overview of Facilitating Efficient Message Queuing

4.1 Overview of an Efficient Message Queuing Service

4.2 Efficient Message Queueing Methods

5.0 Illustrative Hardware System

1.0 General Overview

Modern data centers and other computing environments can compriseanywhere from a few host computer systems to thousands of systemsconfigured to process data, service requests from remote clients, andperform numerous other computational tasks. During operation, variouscomponents within these computing environments often generatesignificant volumes of machine-generated data. For example, machine datais generated by various components in the information technology (IT)environments, such as servers, sensors, routers, mobile devices,Internet of Things (IoT) devices, etc. Machine-generated data caninclude system logs, network packet data, sensor data, applicationprogram data, error logs, stack traces, system performance data, etc. Ingeneral, machine-generated data can also include performance data,diagnostic information, and many other types of data that can beanalyzed to diagnose performance problems, monitor user interactions,and to derive other insights.

A number of tools are available to analyze machine data, that is,machine-generated data. In order to reduce the size of the potentiallyvast amount of machine data that may be generated, many of these toolstypically pre-process the data based on anticipated data-analysis needs.For example, pre-specified data items may be extracted from the machinedata and stored in a database to facilitate efficient retrieval andanalysis of those data items at search time. However, the rest of themachine data typically is not saved and discarded during pre-processing.As storage capacity becomes progressively cheaper and more plentiful,there are fewer incentives to discard these portions of machine data andmany reasons to retain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. In another example, mobile devices maygenerate large amounts of information relating to data accesses,application performance, operating system performance, networkperformance, etc. There can be millions of mobile devices that reportthese types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system isthe leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and searchmachine-generated data from various websites, applications, servers,networks, and mobile devices that power their businesses. The SPLUNK®ENTERPRISE system is particularly useful for analyzing data which iscommonly found in system log files, network data, and other data inputsources. Although many of the techniques described herein are explainedwith reference to a data intake and query system similar to the SPLUNK®ENTERPRISE system, these techniques are also applicable to other typesof data systems.

In the SPLUNK® ENTERPRISE system, machine-generated data are collectedand stored as “events”. An event comprises a portion of themachine-generated data and is associated with a specific point in time.For example, events may be derived from “time series data,” where thetime series data comprises a sequence of data points (e.g., performancemeasurements from a computer system, etc.) that are associated withsuccessive points in time. In general, each event can be associated witha timestamp that is derived from the raw data in the event, determinedthrough interpolation between temporally proximate events having knowntimestamps, or determined based on other configurable rules forassociating timestamps with events, etc.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data stored asfields in a database table. In other instances, machine data may nothave a predefined format, that is, the data is not at fixed, predefinedlocations, but the data does have repeatable patterns and is not random.This means that some machine data can comprise various data items ofdifferent data types and that may be stored at different locationswithin the data. For example, when the data source is an operatingsystem log, an event can include one or more lines from the operatingsystem log containing raw data that includes different types ofperformance and diagnostic information associated with a specific pointin time.

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The data generated by such datasources can include, for example and without limitation, server logfiles, activity log files, configuration files, messages, network packetdata, performance measurements, sensor measurements, etc.

The SPLUNK® ENTERPRISE system uses flexible schema to specify how toextract information from the event data. A flexible schema may bedeveloped and redefined as needed. Note that a flexible schema may beapplied to event data “on the fly,” when it is needed (e.g., at searchtime, index time, ingestion time, etc.). When the schema is not appliedto event data until search time it may be referred to as a “late-bindingschema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw inputdata (e.g., one or more system logs, streams of network packet data,sensor data, application program data, error logs, stack traces, systemperformance data, etc.). The system divides this raw data into blocks(e.g., buckets of data, each associated with a specific time frame,etc.), and parses the raw data to produce timestamped events. The systemstores the timestamped events in a data store. The system enables usersto run queries against the stored data to, for example, retrieve eventsthat meet criteria specified in a query, such as containing certainkeywords or having specific values in defined fields. As used hereinthroughout, data that is part of an event is referred to as “eventdata”. In this context, the term “field” refers to a location in theevent data containing one or more values for a specific data item. Aswill be described in more detail herein, the fields are defined byextraction rules (e.g., regular expressions) that derive one or morevalues from the portion of raw machine data in each event that has aparticular field specified by an extraction rule. The set of values soproduced are semantically-related (such as IP address), even though theraw machine data in each event may be in different formats (e.g.,semantically-related values may be in different positions in the eventsderived from different sources).

As noted above, the SPLUNK® ENTERPRISE system utilizes a late-bindingschema to event data while performing queries on events. One aspect of alate-binding schema is applying “extraction rules” to event data toextract values for specific fields during search time. Morespecifically, the extraction rules for a field can include one or moreinstructions that specify how to extract a value for the field from theevent data. An extraction rule can generally include any type ofinstruction for extracting values from data in events. In some cases, anextraction rule comprises a regular expression where a sequence ofcharacters form a search pattern, in which case the rule is referred toas a “regex rule.” The system applies the regex rule to the event datato extract values for associated fields in the event data by searchingthe event data for the sequence of characters defined in the regex rule.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain field values in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields in a query maybe provided in the query itself, or may be located during execution ofthe query. Hence, as a user learns more about the data in the events,the user can continue to refine the late-binding schema by adding newfields, deleting fields, or modifying the field extraction rules for usethe next time the schema is used by the system. Because the SPLUNK®ENTERPRISE system maintains the underlying raw data and useslate-binding schema for searching the raw data, it enables a user tocontinue investigating and learn valuable insights about the raw data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by disparate data sources, thesystem facilitates use of a “common information model” (CIM) across thedisparate data sources (further discussed with respect to FIG. 5).

2.0. Operating Environment

FIG. 1 illustrates a networked computer system 100 in which anembodiment may be implemented. Those skilled in the art would understandthat FIG. 1 represents one example of a networked computer system andother embodiments may use different arrangements.

The networked computer system 100 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In an embodiment, one or more client devices 102 are coupled to one ormore host devices 106 and a data intake and query system 108 via one ormore networks 104. Networks 104 broadly represent one or more LANs,WANs, cellular networks (e.g., LTE, HSPA, 3G, and other cellulartechnologies), and/or networks using any of wired, wireless, terrestrialmicrowave, or satellite links, and may include the public Internet.

2.1. Host Devices

In the illustrated embodiment, a system 100 includes one or more hostdevices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, and/or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HTML documents, media content, etc. Thecommunication between a client device 102 and host application 114 mayinclude sending various requests and receiving data packets. Forexample, in general, a client device 102 or application running on aclient device may initiate communication with a host application 114 bymaking a request for a specific resource (e.g., based on an HTTPrequest), and the application server may respond with the requestedcontent stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 114 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types ofmachine-generated data. For example, a host application 114 comprising aweb server may generate one or more web server logs in which details ofinteractions between the web server and any number of client devices 102is recorded. As another example, a host device 106 comprising a routermay generate one or more router logs that record information related tonetwork traffic managed by the router. As yet another example, a hostapplication 114 comprising a database server may generate one or morelogs that record information related to requests sent from other hostapplications 114 (e.g., web servers or application servers) for datamanaged by the database server.

2.2. Client Devices

Client devices 102 of FIG. 1 represent any computing device capable ofinteracting with one or more host devices 106 via a network 104.Examples of client devices 102 may include, without limitation, smartphones, tablet computers, handheld computers, wearable devices, laptopcomputers, desktop computers, servers, portable media players, gamingdevices, and so forth. In general, a client device 102 can provideaccess to different content, for instance, content provided by one ormore host devices 106, etc. Each client device 102 may comprise one ormore client applications 110, described in more detail in a separatesection hereinafter.

2.3. Client Device Applications

In an embodiment, each client device 102 may host or execute one or moreclient applications 110 that are capable of interacting with one or morehost devices 106 via one or more networks 104. For instance, a clientapplication 110 may be or comprise a web browser that a user may use tonavigate to one or more websites or other resources provided by one ormore host devices 106. As another example, a client application 110 maycomprise a mobile application or “app.” For example, an operator of anetwork-based service hosted by one or more host devices 106 may makeavailable one or more mobile apps that enable users of client devices102 to access various resources of the network-based service. As yetanother example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In an embodiment, a client application 110 may include a monitoringcomponent 112. At a high level, the monitoring component 112 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's operating state, includingmonitoring network traffic sent and received from the client device andcollecting other device and/or application-specific information.Monitoring component 112 may be an integrated component of a clientapplication 110, a plug-in, an extension, or any other type of add-oncomponent. Monitoring component 112 may also be a stand-alone process.

In one embodiment, a monitoring component 112 may be created when aclient application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself.

In some cases, an SDK or other code for implementing the monitoringfunctionality may be offered by a provider of a data intake and querysystem, such as a system 108. In such cases, the provider of the system108 can implement the custom code so that performance data generated bythe monitoring functionality is sent to the system 108 to facilitateanalysis of the performance data by a developer of the clientapplication or other users.

In an embodiment, the custom monitoring code may be incorporated intothe code of a client application 110 in a number of different ways, suchas the insertion of one or more lines in the client application codethat call or otherwise invoke the monitoring component 112. As such, adeveloper of a client application 110 can add one or more lines of codeinto the client application 110 to trigger the monitoring component 112at desired points during execution of the application. Code thattriggers the monitoring component may be referred to as a monitortrigger. For instance, a monitor trigger may be included at or near thebeginning of the executable code of the client application 110 such thatthe monitoring component 112 is initiated or triggered as theapplication is launched, or included at other points in the code thatcorrespond to various actions of the client application, such as sendinga network request or displaying a particular interface.

In an embodiment, the monitoring component 112 may monitor one or moreaspects of network traffic sent and/or received by a client application110. For example, the monitoring component 112 may be configured tomonitor data packets transmitted to and/or from one or more hostapplications 114. Incoming and/or outgoing data packets can be read orexamined to identify network data contained within the packets, forexample, and other aspects of data packets can be analyzed to determinea number of network performance statistics. Monitoring network trafficmay enable information to be gathered particular to the networkperformance associated with a client application 110 or set ofapplications.

In an embodiment, network performance data refers to any type of datathat indicates information about the network and/or network performance.Network performance data may include, for instance, a URL requested, aconnection type (e.g., HTTP, HTTPS, etc.), a connection start time, aconnection end time, an HTTP status code, request length, responselength, request headers, response headers, connection status (e.g.,completion, response time(s), failure, etc.), and the like. Uponobtaining network performance data indicating performance of thenetwork, the network performance data can be transmitted to a dataintake and query system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 can be distributed to clientdevices 102. Applications generally can be distributed to client devices102 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In an embodiment, the monitoring component 112 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 110 and/or client device 102.For example, a monitoring component 112 may be configured to collectdevice performance information by monitoring one or more client deviceoperations, or by making calls to an operating system and/or one or moreother applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In an embodiment, the monitoring component 112 may also monitor andcollect other device profile information including, for example, a typeof client device, a manufacturer and model of the device, versions ofvarious software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“networkLatency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

2.4. Data Server System

FIG. 2 depicts a block diagram of an exemplary data intake and querysystem 108, similar to the SPLUNK® ENTERPRISE system. System 108includes one or more forwarders 204 that receive data from a variety ofinput data sources 202, and one or more indexers 206 that process andstore the data in one or more data stores 208. These forwarders andindexers can comprise separate computer systems, or may alternativelycomprise separate processes executing on one or more computer systems.

Each data source 202 broadly represents a distinct source of data thatcan be consumed by a system 108. Examples of a data source 202 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the forwarders 204 identify which indexers 206 receivedata collected from a data source 202 and forward the data to theappropriate indexers. Forwarders 204 can also perform operations on thedata before forwarding, including removing extraneous data, detectingtimestamps in the data, parsing data, indexing data, routing data basedon criteria relating to the data being routed, and/or performing otherdata transformations.

In an embodiment, a forwarder 204 may comprise a service accessible toclient devices 102 and host devices 106 via a network 104. For example,one type of forwarder 204 may be capable of consuming vast amounts ofreal-time data from a potentially large number of client devices 102and/or host devices 106. The forwarder 204 may, for example, comprise acomputing device which implements multiple data pipelines or “queues” tohandle forwarding of network data to indexers 206. A forwarder 204 mayalso perform many of the functions that are performed by an indexer. Forexample, a forwarder 204 may perform keyword extractions on raw data orparse raw data to create events. A forwarder 204 may generate timestamps for events. Additionally or alternatively, a forwarder 204 mayperform routing of events to indexers. Data store 208 may contain eventsderived from machine data from a variety of sources all pertaining tothe same component in an IT environment, and this data may be producedby the machine in question or by other components in the IT environment.

2.5. Data Ingestion

FIG. 3 depicts a flow chart illustrating an example data flow performedby Data Intake and Query system 108, in accordance with the disclosedembodiments. The data flow illustrated in FIG. 3 is provided forillustrative purposes only; those skilled in the art would understandthat one or more of the steps of the processes illustrated in FIG. 3 maybe removed or the ordering of the steps may be changed. Furthermore, forthe purposes of illustrating a clear example, one or more particularsystem components are described in the context of performing variousoperations during each of the data flow stages. For example, a forwarderis described as receiving and processing data during an input phase; anindexer is described as parsing and indexing data during parsing andindexing phases; and a search head is described as performing a searchquery during a search phase. However, other system arrangements anddistributions of the processing steps across system components may beused.

2.5.1. Input

At block 302, a forwarder receives data from an input source, such as adata source 202 shown in FIG. 2. A forwarder initially may receive thedata as a raw data stream generated by the input source. For example, aforwarder may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. In one embodiment, a forwarderreceives the raw data and may segment the data stream into “blocks”, or“buckets,” possibly of a uniform data size, to facilitate subsequentprocessing steps.

At block 304, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or IP address of a device that generatedthe data. A source field may contain a value identifying a source of thedata, such as a pathname of a file or a protocol and port related toreceived network data. A source type field may contain a valuespecifying a particular source type label for the data. Additionalmetadata fields may also be included during the input phase, such as acharacter encoding of the data, if known, and possibly other values thatprovide information relevant to later processing steps. In anembodiment, a forwarder forwards the annotated data blocks to anothersystem component (typically an indexer) for further processing.

The SPLUNK® ENTERPRISE system allows forwarding of data from one SPLUNK®ENTERPRISE instance to another, or even to a third-party system. SPLUNK®ENTERPRISE system can employ different types of forwarders in aconfiguration.

In an embodiment, a forwarder may contain the essential componentsneeded to forward data. It can gather data from a variety of inputs andforward the data to a SPLUNK® ENTERPRISE server for indexing andsearching. It also can tag metadata (e.g., source, source type, host,etc.).

Additionally or optionally, in an embodiment, a forwarder has thecapabilities of the aforementioned forwarder as well as additionalcapabilities. The forwarder can parse data before forwarding the data(e.g., associate a time stamp with a portion of data and create anevent, etc.) and can route data based on criteria such as source or typeof event. It can also index data locally while forwarding the data toanother indexer.

2.5.2. Parsing

At block 306, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. In an embodiment, toorganize the data into events, an indexer may determine a source typeassociated with each data block (e.g., by extracting a source type labelfrom the metadata fields associated with the data block, etc.) and referto a source type configuration corresponding to the identified sourcetype. The source type definition may include one or more properties thatindicate to the indexer to automatically determine the boundaries ofevents within the data. In general, these properties may include regularexpression-based rules or delimiter rules where, for example, eventboundaries may be indicated by predefined characters or characterstrings. These predefined characters may include punctuation marks orother special characters including, for example, carriage returns, tabs,spaces, line breaks, etc. If a source type for the data is unknown tothe indexer, an indexer may infer a source type for the data byexamining the structure of the data. Then, it can apply an inferredsource type definition to the data to create the events.

At block 308, the indexer determines a timestamp for each event. Similarto the process for creating events, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data in the event, to interpolatetime values based on timestamps associated with temporally proximateevents, to create a timestamp based on a time the event data wasreceived or generated, to use the timestamp of a previous event, or useany other rules for determining timestamps.

At block 310, the indexer associates with each event one or moremetadata fields including a field containing the timestamp (in someembodiments, a timestamp may be included in the metadata fields)determined for the event. These metadata fields may include a number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 304, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp.

At block 312, an indexer may optionally apply one or moretransformations to data included in the events created at block 306. Forexample, such transformations can include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to event data may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

2.5.3. Indexing

At blocks 314 and 316, an indexer can optionally generate a keywordindex to facilitate fast keyword searching for event data. To build akeyword index, at block 314, the indexer identifies a set of keywords ineach event. At block 316, the indexer includes the identified keywordsin an index, which associates each stored keyword with referencepointers to events containing that keyword (or to locations withinevents where that keyword is located, other location identifiers, etc.).When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, where a name-value pair can include apair of keywords connected by a symbol, such as an equals sign or colon.This way, events containing these name-value pairs can be quicklylocated. In some embodiments, fields can automatically be generated forsome or all of the name-value pairs at the time of indexing. Forexample, if the string “dest=10.0.1.2” is found in an event, a fieldnamed “dest” may be created for the event, and assigned a value of“10.0.1.2”.

At block 318, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to search for events basedon a time range. In one embodiment, the stored events are organized into“buckets,” where each bucket stores events associated with a specifictime range based on the timestamps associated with each event. This maynot only improve time-based searching, but also allows for events withrecent timestamps, which may have a higher likelihood of being accessed,to be stored in a faster memory to facilitate faster retrieval. Forexample, buckets containing the most recent events can be stored inflash memory rather than on a hard disk.

Each indexer 206 may be responsible for storing and searching a subsetof the events contained in a corresponding data store 208. Bydistributing events among the indexers and data stores, the indexers cananalyze events for a query in parallel. For example, using map-reducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize data retrieval process by searching bucketscorresponding to time ranges that are relevant to a query.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. patent application Ser. No. 14/266,812,entitled “SITE-BASED SEARCH AFFINITY”, filed on 30 Apr. 2014, and inU.S. patent application Ser. No. 14/266,817, entitled “MULTI-SITECLUSTERING”, also filed on 30 Apr. 2014, each of which is herebyincorporated by reference in its entirety for all purposes.

2.6. Query Processing

FIG. 4 is a flow diagram that illustrates an exemplary process that asearch head and one or more indexers may perform during a search query.At block 402, a search head receives a search query from a client. Atblock 404, the search head analyzes the search query to determine whatportion(s) of the query can be delegated to indexers and what portionsof the query can be executed locally by the search head. At block 406,the search head distributes the determined portions of the query to theappropriate indexers. In an embodiment, a search head cluster may takethe place of an independent search head where each search head in thesearch head cluster coordinates with peer search heads in the searchhead cluster to schedule jobs, replicate search results, updateconfigurations, fulfill search requests, etc. In an embodiment, thesearch head (or each search head) communicates with a master node (alsoknown as a cluster master, not shown in Fig.) that provides the searchhead with a list of indexers to which the search head can distribute thedetermined portions of the query. The master node maintains a list ofactive indexers and can also designate which indexers may haveresponsibility for responding to queries over certain sets of events. Asearch head may communicate with the master node before the search headdistributes queries to indexers to discover the addresses of activeindexers.

At block 408, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The searching operations at block 408 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In an embodiment, one or more rulesfor extracting field values may be specified as part of a source typedefinition. The indexers may then either send the relevant events backto the search head, or use the events to determine a partial result, andsend the partial result back to the search head.

At block 410, the search head combines the partial results and/or eventsreceived from the indexers to produce a final result for the query. Thisfinal result may comprise different types of data depending on what thequery requested. For example, the results can include a listing ofmatching events returned by the query, or some type of visualization ofthe data from the returned events. In another example, the final resultcan include one or more calculated values derived from the matchingevents.

The results generated by the system 108 can be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries that are performedon a periodic basis.

2.7. Field Extraction

The search head 210 allows users to search and visualize event dataextracted from raw machine data received from homogenous data sources.It also allows users to search and visualize event data extracted fromraw machine data received from heterogeneous data sources. The searchhead 210 includes various mechanisms, which may additionally reside inan indexer 206, for processing a query. Splunk Processing Language(SPL), used in conjunction with the SPLUNK® ENTERPRISE system, can beutilized to make a query. SPL is a pipelined search language in which aset of inputs is operated on by a first command in a command line, andthen a subsequent command following the pipe symbol “|” operates on theresults produced by the first command, and so on for additionalcommands. Other query languages, such as the Structured Query Language(“SQL”), can be used to create a query.

In response to receiving the search query, search head 210 usesextraction rules to extract values for the fields associated with afield or fields in the event data being searched. The search head 210obtains extraction rules that specify how to extract a value for certainfields from an event. Extraction rules can comprise regex rules thatspecify how to extract values for the relevant fields. In addition tospecifying how to extract field values, the extraction rules may alsoinclude instructions for deriving a field value by performing a functionon a character string or value retrieved by the extraction rule. Forexample, a transformation rule may truncate a character string, orconvert the character string into a different data format. In somecases, the query itself can specify one or more extraction rules.

The search head 210 can apply the extraction rules to event data that itreceives from indexers 206. Indexers 206 may apply the extraction rulesto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

FIG. 5 illustrates an example of raw machine data received fromdisparate data sources. In this example, a user submits an order formerchandise using a vendor's shopping application program 501 running onthe user's system. In this example, the order was not delivered to thevendor's server due to a resource exception at the destination serverthat is detected by the middleware code 502. The user then sends amessage to the customer support 503 to complain about the order failingto complete. The three systems 501, 502, and 503 are disparate systemsthat do not have a common logging format. The order application 501sends log data 504 to the SPLUNK® ENTERPRISE system in one format, themiddleware code 502 sends error log data 505 in a second format, and thesupport server 503 sends log data 506 in a third format.

Using the log data received at one or more indexers 206 from the threesystems the vendor can uniquely obtain an insight into user activity,user experience, and system behavior. The search head 210 allows thevendor's administrator to search the log data from the three systemsthat one or more indexers 206 are responsible for searching, therebyobtaining correlated information, such as the order number andcorresponding customer ID number of the person placing the order. Thesystem also allows the administrator to see a visualization of relatedevents via a user interface. The administrator can query the search head210 for customer ID field value matches across the log data from thethree systems that are stored at the one or more indexers 206. Thecustomer ID field value exists in the data gathered from the threesystems, but the customer ID field value may be located in differentareas of the data given differences in the architecture of thesystems—there is a semantic relationship between the customer ID fieldvalues generated by the three systems. The search head 210 requestsevent data from the one or more indexers 206 to gather relevant eventdata from the three systems. It then applies extraction rules to theevent data in order to extract field values that it can correlate. Thesearch head may apply a different extraction rule to each set of eventsfrom each system when the event data format differs among systems. Inthis example, the user interface can display to the administrator theevent data corresponding to the common customer ID field values 507,508, and 509, thereby providing the administrator with insight into acustomer's experience.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, or avisualization, such as a graph or chart, generated from the values.

2.8. Example Search Screen

FIG. 6A illustrates an example search screen 600 in accordance with thedisclosed embodiments. Search screen 600 includes a search bar 602 thataccepts user input in the form of a search string. It also includes atime range picker 612 that enables the user to specify a time range forthe search. For “historical searches” the user can select a specifictime range, or alternatively a relative time range, such as “today,”“yesterday” or “last week.” For “real-time searches,” the user canselect the size of a preceding time window to search for real-timeevents. Search screen 600 also initially displays a “data summary”dialog as is illustrated in FIG. 6B that enables the user to selectdifferent sources for the event data, such as by selecting specifichosts and log files.

After the search is executed, the search screen 600 in FIG. 6A candisplay the results through search results tabs 604, wherein searchresults tabs 604 includes: an “events tab” that displays variousinformation about events returned by the search; a “statistics tab” thatdisplays statistics about the search results; and a “visualization tab”that displays various visualizations of the search results. The eventstab illustrated in FIG. 6A displays a timeline graph 605 thatgraphically illustrates the number of events that occurred in one-hourintervals over the selected time range. It also displays an events list608 that enables a user to view the raw data in each of the returnedevents. It additionally displays a fields sidebar 606 that includesstatistics about occurrences of specific fields in the returned events,including “selected fields” that are pre-selected by the user, and“interesting fields” that are automatically selected by the system basedon pre-specified criteria.

2.9. Data Models

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge necessary to build a variety of specialized searches of thosedatasets. Those searches, in turn, can be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.

Objects in data models can be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Data model objects are defined bycharacteristics that mostly break down into constraints and attributes.Child objects inherit constraints and attributes from their parentobjects and have additional constraints and attributes of their own.Child objects provide a way of filtering events from parent objects.Because a child object always provides an additional constraint inaddition to the constraints it has inherited from its parent object, thedataset it represents is always a subset of the dataset that its parentrepresents.

For example, a first data model object may define a broad set of datapertaining to e-mail activity generally, and another data model objectmay define specific datasets within the broad dataset, such as a subsetof the e-mail data pertaining specifically to e-mails sent. Examples ofdata models can include electronic mail, authentication, databases,intrusion detection, malware, application state, alerts, computeinventory, network sessions, network traffic, performance, audits,updates, vulnerabilities, etc. Data models and their objects can bedesigned by knowledge managers in an organization, and they can enabledownstream users to quickly focus on a specific set of data. Forexample, a user can simply select an “e-mail activity” data model objectto access a dataset relating to e-mails generally (e.g., sent orreceived), or select an “e-mails sent” data model object (or datasub-model object) to access a dataset relating to e-mails sent.

A data model object may be defined by (1) a set of search constraints,and (2) a set of fields. Thus, a data model object can be used toquickly search data to identify a set of events and to identify a set offields to be associated with the set of events. For example, an “e-mailssent” data model object may specify a search for events relating toe-mails that have been sent, and specify a set of fields that areassociated with the events. Thus, a user can retrieve and use the“e-mails sent” data model object to quickly search source data forevents relating to sent e-mails, and may be provided with a listing ofthe set of fields relevant to the events in a user interface screen.

A child of the parent data model may be defined by a search (typically anarrower search) that produces a subset of the events that would beproduced by the parent data model's search. The child's set of fieldscan include a subset of the set of fields of the parent data modeland/or additional fields. Data model objects that reference the subsetscan be arranged in a hierarchical manner, so that child subsets ofevents are proper subsets of their parents. A user iteratively applies amodel development tool (not shown in Fig.) to prepare a query thatdefines a subset of events and assigns an object name to that subset. Achild subset is created by further limiting a query that generated aparent subset. A late-binding schema of field extraction rules isassociated with each object or subset in the data model.

Data definitions in associated schemas can be taken from the commoninformation model (CIM) or can be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand can include fields not present in parents. A model developer canselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules can be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issuedon 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar. 2015, U.S. patentapplication Ser. No. 14/611,232, entitled “GENERATION OF A DATA MODELAPPLIED TO QUERIES”, filed on 31 Jan. 2015, and U.S. patent applicationSer. No. 14/815,884, entitled “GENERATION OF A DATA MODEL APPLIED TOOBJECT QUERIES”, filed on 31 Jul. 2015, each of which is herebyincorporated by reference in its entirety for all purposes. See, also,Knowledge Manager Manual, Build a Data Model, Splunk Enterprise 6.1.3pp. 150-204 (Aug. 25, 2014).

A data model can also include reports. One or more report formats can beassociated with a particular data model and be made available to runagainst the data model. A user can use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In an embodiment, the data intake and query system 108 provides the userwith the ability to produce reports (e.g., a table, chart,visualization, etc.) without having to enter SPL, SQL, or other querylanguage terms into a search screen. Data models are used as the basisfor the search feature.

Data models may be selected in a report generation interface. The reportgenerator supports drag-and-drop organization of fields to be summarizedin a report. When a model is selected, the fields with availableextraction rules are made available for use in the report. The user mayrefine and/or filter search results to produce more precise reports. Theuser may select some fields for organizing the report and select otherfields for providing detail according to the report organization. Forexample, “region” and “salesperson” are fields used for organizing thereport and sales data can be summarized (subtotaled and totaled) withinthis organization. The report generator allows the user to specify oneor more fields within events and apply statistical analysis on valuesextracted from the specified one or more fields. The report generatormay aggregate search results across sets of events and generatestatistics based on aggregated search results. Building reports usingthe report generation interface is further explained in U.S. patentapplication Ser. No. 14/503,335, entitled “GENERATING REPORTS FROMUNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is herebyincorporated by reference in its entirety for all purposes, and in PivotManual, Splunk Enterprise 6.1.3 (Aug. 4, 2014). Data visualizations alsocan be generated in a variety of formats, by reference to the datamodel. Reports, data visualizations, and data model objects can be savedand associated with the data model for future use. The data model objectmay be used to perform searches of other data.

FIGS. 12, 13, and 7A-7D illustrate a series of user interface screenswhere a user may select report generation options using data models. Thereport generation process may be driven by a predefined data modelobject, such as a data model object defined and/or saved via a reportingapplication or a data model object obtained from another source. A usercan load a saved data model object using a report editor. For example,the initial search query and fields used to drive the report editor maybe obtained from a data model object. The data model object that is usedto drive a report generation process may define a search and a set offields. Upon loading of the data model object, the report generationprocess may enable a user to use the fields (e.g., the fields defined bythe data model object) to define criteria for a report (e.g., filters,split rows/columns, aggregates, etc.) and the search may be used toidentify events (e.g., to identify events responsive to the search) usedto generate the report. That is, for example, if a data model object isselected to drive a report editor, the graphical user interface of thereport editor may enable a user to define reporting criteria for thereport using the fields associated with the selected data model object,and the events used to generate the report may be constrained to theevents that match, or otherwise satisfy, the search constraints of theselected data model object.

The selection of a data model object for use in driving a reportgeneration may be facilitated by a data model object selectioninterface. FIG. 12 illustrates an example interactive data modelselection graphical user interface 1200 of a report editor that displaysa listing of available data models 1201. The user may select one of thedata models 1202.

FIG. 13 illustrates an example data model object selection graphicaluser interface 1300 that displays available data objects 1301 for theselected data object model 1202. The user may select one of thedisplayed data model objects 1302 for use in driving the reportgeneration process.

Once a data model object is selected by the user, a user interfacescreen 700 shown in FIG. 7A may display an interactive listing ofautomatic field identification options 701 based on the selected datamodel object. For example, a user may select one of the threeillustrated options (e.g., the “All Fields” option 702, the “SelectedFields” option 703, or the “Coverage” option (e.g., fields with at leasta specified % of coverage) 704). If the user selects the “All Fields”option 702, all of the fields identified from the events that werereturned in response to an initial search query may be selected. Thatis, for example, all of the fields of the identified data model objectfields may be selected. If the user selects the “Selected Fields” option703, only the fields from the fields of the identified data model objectfields that are selected by the user may be used. If the user selectsthe “Coverage” option 704, only the fields of the identified data modelobject fields meeting a specified coverage criteria may be selected. Apercent coverage may refer to the percentage of events returned by theinitial search query that a given field appears in. Thus, for example,if an object dataset includes 10,000 events returned in response to aninitial search query, and the “avg_age” field appears in 854 of those10,000 events, then the “avg_age” field would have a coverage of 8.54%for that object dataset. If, for example, the user selects the“Coverage” option and specifies a coverage value of 2%, only fieldshaving a coverage value equal to or greater than 2% may be selected. Thenumber of fields corresponding to each selectable option may bedisplayed in association with each option. For example, “97” displayednext to the “All Fields” option 702 indicates that 97 fields will beselected if the “All Fields” option is selected. The “3” displayed nextto the “Selected Fields” option 703 indicates that 3 of the 97 fieldswill be selected if the “Selected Fields” option is selected. The “49”displayed next to the “Coverage” option 704 indicates that 49 of the 97fields (e.g., the 49 fields having a coverage of 2% or greater) will beselected if the “Coverage” option is selected. The number of fieldscorresponding to the “Coverage” option may be dynamically updated basedon the specified percent of coverage.

FIG. 7B illustrates an example graphical user interface screen (alsocalled the pivot interface) 705 displaying the reporting application's“Report Editor” page. The screen may display interactive elements fordefining various elements of a report. For example, the page includes a“Filters” element 706, a “Split Rows” element 707, a “Split Columns”element 708, and a “Column Values” element 709. The page may include alist of search results 711. In this example, the Split Rows element 707is expanded, revealing a listing of fields 710 that can be used todefine additional criteria (e.g., reporting criteria). The listing offields 710 may correspond to the selected fields (attributes). That is,the listing of fields 710 may list only the fields previously selected,either automatically and/or manually by a user. FIG. 7C illustrates aformatting dialogue 712 that may be displayed upon selecting a fieldfrom the listing of fields 710. The dialogue can be used to format thedisplay of the results of the selection (e.g., label the column to bedisplayed as “component”).

FIG. 7D illustrates an example graphical user interface screen 705including a table of results 713 based on the selected criteriaincluding splitting the rows by the “component” field. A column 714having an associated count for each component listed in the table may bedisplayed that indicates an aggregate count of the number of times thatthe particular field-value pair (e.g., the value in a row) occurs in theset of events responsive to the initial search query.

FIG. 14 illustrates an example graphical user interface screen 1400 thatallows the user to filter search results and to perform statisticalanalysis on values extracted from specific fields in the set of events.In this example, the top ten product names ranked by price are selectedas a filter 1401 that causes the display of the ten most popularproducts sorted by price. Each row is displayed by product name andprice 1402. This results in each product displayed in a column labeled“product name” along with an associated price in a column labeled“price” 1406. Statistical analysis of other fields in the eventsassociated with the ten most popular products have been specified ascolumn values 1403. A count of the number of successful purchases foreach product is displayed in column 1404. These statistics may beproduced by filtering the search results by the product name, findingall occurrences of a successful purchase in a field within the eventsand generating a total of the number of occurrences. A sum of the totalsales is displayed in column 1405, which is a result of themultiplication of the price and the number of successful purchases foreach product.

The reporting application allows the user to create graphicalvisualizations of the statistics generated for a report. For example,FIG. 15 illustrates an example graphical user interface 1500 thatdisplays a set of components and associated statistics 1501. Thereporting application allows the user to select a visualization of thestatistics in a graph (e.g., bar chart, scatter plot, area chart, linechart, pie chart, radial gauge, marker gauge, filler gauge, etc.). FIG.16 illustrates an example of a bar chart visualization 1600 of an aspectof the statistical data 1501. FIG. 17 illustrates a scatter plotvisualization 1700 of an aspect of the statistical data 1501.

2.10. Acceleration Technique

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally processed data “on thefly” at search time instead of storing pre-specified portions of thedata in a database at ingestion time. This flexibility enables a user tosee valuable insights, correlate data, and perform subsequent queries toexamine interesting aspects of the data that may not have been apparentat ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause delays in processing thequeries. Advantageously, SPLUNK® ENTERPRISE system employs a number ofunique acceleration techniques that have been developed to speed upanalysis operations performed at search time. These techniques include:(1) performing search operations in parallel across multiple indexers;(2) using a keyword index; (3) using a high performance analytics store;and (4) accelerating the process of generating reports. These noveltechniques are described in more detail below.

2.10.1. Aggregation Technique

To facilitate faster query processing, a query can be structured suchthat multiple indexers perform the query in parallel, while aggregationof search results from the multiple indexers is performed locally at thesearch head. For example, FIG. 8 illustrates how a search query 802received from a client at a search head 210 can split into two phases,including: (1) subtasks 804 (e.g., data retrieval or simple filtering)that may be performed in parallel by indexers 206 for execution, and (2)a search results aggregation operation 806 to be executed by the searchhead when the results are ultimately collected from the indexers.

During operation, upon receiving search query 802, a search head 210determines that a portion of the operations involved with the searchquery may be performed locally by the search head. The search headmodifies search query 802 by substituting “stats” (create aggregatestatistics over results sets received from the indexers at the searchhead) with “prestats” (create statistics by the indexer from localresults set) to produce search query 804, and then distributes searchquery 804 to distributed indexers, which are also referred to as “searchpeers.” Note that search queries may generally specify search criteriaor operations to be performed on events that meet the search criteria.Search queries may also specify field names, as well as search criteriafor the values in the fields or operations to be performed on the valuesin the fields. Moreover, the search head may distribute the full searchquery to the search peers as illustrated in FIG. 4, or may alternativelydistribute a modified version (e.g., a more restricted version) of thesearch query to the search peers. In this example, the indexers areresponsible for producing the results and sending them to the searchhead. After the indexers return the results to the search head, thesearch head aggregates the received results 806 to form a single searchresult set. By executing the query in this manner, the systemeffectively distributes the computational operations across the indexerswhile minimizing data transfers.

2.10.2. Keyword Index

As described above with reference to the flow charts in FIG. 3 and FIG.4, data intake and query system 108 can construct and maintain one ormore keyword indices to quickly identify events containing specifickeywords. This technique can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex, an indexer first identifies a set of keywords. Then, the indexerincludes the identified keywords in an index, which associates eachstored keyword with references to events containing that keyword, or tolocations within events where that keyword is located. When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

2.10.3. High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 108create a high performance analytics store, which is referred to as a“summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the event data and includes references toevents containing the specific value in the specific field. For example,an example entry in a summarization table can keep track of occurrencesof the value “94107” in a “ZIP code” field of a set of events and theentry includes references to all of the events that contain the value“94107” in the ZIP code field. This optimization technique enables thesystem to quickly process queries that seek to determine how many eventshave a particular value for a particular field. To this end, the systemcan examine the entry in the summarization table to count instances ofthe specific value in the field without having to go through theindividual events or perform data extractions at search time. Also, ifthe system needs to process all events that have a specific field-valuecombination, the system can use the references in the summarizationtable entry to directly access the events to extract further informationwithout having to search all of the events to find the specificfield-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range. A bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer. The indexer-specificsummarization table includes entries for the events in a data store thatare managed by the specific indexer. Indexer-specific summarizationtables may also be bucket-specific.

The summarization table can be populated by running a periodic querythat scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A periodic query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Aperiodic query can also be automatically launched in response to a querythat asks for a specific field-value combination.

In some cases, when the summarization tables may not cover all of theevents that are relevant to a query, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query. Thesummarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, entitled “DISTRIBUTED HIGHPERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014, U.S. patentapplication Ser. No. 14/170,159, entitled “SUPPLEMENTING A HIGHPERFORMANCE ANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TORESPOND TO AN EVENT QUERY”, filed on 31 Jan. 2014, and U.S. patentapplication Ser. No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROLDEVICE”, filed on 21 Feb. 2014, each of which is hereby incorporated byreference in its entirety.

2.10.4. Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. If reports can be accelerated, the summarizationengine periodically generates a summary covering data obtained during alatest non-overlapping time period. For example, where the query seeksevents meeting a specified criteria, a summary for the time periodincludes only events within the time period that meet the specifiedcriteria. Similarly, if the query seeks statistics calculated from theevents, such as the number of events that match the specified criteria,then the summary for the time period includes the number of events inthe period that match the specified criteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-miming the query for previous time periods, so advantageously onlythe newer event data needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “COMPRESSED JOURNALING INEVENT TRACKING FILES FOR METADATA RECOVERY AND REPLICATION”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “REAL TIME SEARCHING ANDREPORTING”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety.

2.11. Security Features

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards andvisualizations that simplify developers' task to create applicationswith additional capabilities. One such application is the SPLUNK® APPFOR ENTERPRISE SECURITY, which performs monitoring and alertingoperations and includes analytics to facilitate identifying both knownand unknown security threats based on large volumes of data stored bythe SPLUNK® ENTERPRISE system. SPLUNK® APP FOR ENTERPRISE SECURITYprovides the security practitioner with visibility intosecurity-relevant threats found in the enterprise infrastructure bycapturing, monitoring, and reporting on data from enterprise securitydevices, systems, and applications. Through the use of SPLUNK®ENTERPRISE searching and reporting capabilities, SPLUNK® APP FORENTERPRISE SECURITY provides a top-down and bottom-up view of anorganization's security posture.

The SPLUNK® APP FOR ENTERPRISE SECURITY leverages SPLUNK® ENTERPRISEsearch-time normalization techniques, saved searches, and correlationsearches to provide visibility into security-relevant threats andactivity and generate notable events for tracking. The App enables thesecurity practitioner to investigate and explore the data to find new orunknown threats that do not follow signature-based patterns.

Conventional Security Information and Event Management (SIEM) systemsthat lack the infrastructure to effectively store and analyze largevolumes of security-related data. Traditional SIEM systems typically usefixed schemas to extract data from pre-defined security-related fieldsat data ingestion time and storing the extracted data in a relationaldatabase. This traditional data extraction process (and associatedreduction in data size) that occurs at data ingestion time inevitablyhampers future incident investigations that may need original data todetermine the root cause of a security issue, or to detect the onset ofan impending security threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data and enables a user to define such schemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. Pat. No.8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS INBIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014,U.S. patent application Ser. No. 13/956,252, entitled “INVESTIGATIVE ANDDYNAMIC DETECTION OF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS INBIG DATA”, filed on 31 Jul. 2013, U.S. patent application Ser. No.14/445,018, entitled “GRAPHIC DISPLAY OF SECURITY THREATS BASED ONINDICATIONS OF ACCESS TO NEWLY REGISTERED DOMAINS”, filed on 28 Jul.2014, U.S. patent application Ser. No. 14/445,023, entitled “SECURITYTHREAT DETECTION OF NEWLY REGISTERED DOMAINS”, filed on 28 Jul. 2014,U.S. patent application Ser. No. 14/815,971, entitled “SECURITY THREATDETECTION USING DOMAIN NAME ACCESSES”, filed on 1 Aug. 2015, and U.S.patent application Ser. No. 14/815,972, entitled “SECURITY THREATDETECTION USING DOMAIN NAME REGISTRATIONS”, filed on 1 Aug. 2015, eachof which is hereby incorporated by reference in its entirety for allpurposes. Security-related information can also include malwareinfection data and system configuration information, as well as accesscontrol information, such as login/logout information and access failurenotifications. The security-related information can originate fromvarious sources within a data center, such as hosts, virtual machines,storage devices and sensors. The security-related information can alsooriginate from various sources in a network, such as routers, switches,email servers, proxy servers, gateways, firewalls andintrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting “notable events” that are likely to indicate a securitythreat. These notable events can be detected in a number of ways: (1) auser can notice a correlation in the data and can manually identify acorresponding group of one or more events as “notable;” or (2) a usercan define a “correlation search” specifying criteria for a notableevent, and every time one or more events satisfy the criteria, theapplication can indicate that the one or more events are notable. A usercan alternatively select a pre-defined correlation search provided bythe application. Note that correlation searches can be run continuouslyor at regular intervals (e.g., every hour) to search for notable events.Upon detection, notable events can be stored in a dedicated “notableevents index,” which can be subsequently accessed to generate variousvisualizations containing security-related information. Also, alerts canbe generated to notify system operators when important notable eventsare discovered.

The SPLUNK® APP FOR ENTERPRISE SECURITY provides various visualizationsto aid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics, such as counts ofdifferent types of notable events. For example, FIG. 9A illustrates anexample key indicators view 900 that comprises a dashboard, which candisplay a value 901, for various security-related metrics, such asmalware infections 902. It can also display a change in a metric value903, which indicates that the number of malware infections increased by63 during the preceding interval. Key indicators view 900 additionallydisplays a histogram panel 904 that displays a histogram of notableevents organized by urgency values, and a histogram of notable eventsorganized by time intervals. This key indicators view is described infurther detail in pending U.S. patent application Ser. No. 13/956,338,entitled “KEY INDICATORS VIEW”, filed on 31 Jul. 2013, and which ishereby incorporated by reference in its entirety for all purposes.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 9B illustrates an example incident review dashboard 910 thatincludes a set of incident attribute fields 911 that, for example,enables a user to specify a time range field 912 for the displayedevents. It also includes a timeline 913 that graphically illustrates thenumber of incidents that occurred in time intervals over the selectedtime range. It additionally displays an events list 914 that enables auser to view a list of all of the notable events that match the criteriain the incident attributes fields 911. To facilitate identifyingpatterns among the notable events, each notable event can be associatedwith an urgency value (e.g., low, medium, high, critical), which isindicated in the incident review dashboard. The urgency value for adetected event can be determined based on the severity of the event andthe priority of the system component associated with the event.

2.12. Data Center Monitoring

As mentioned above, the SPLUNK® ENTERPRISE platform provides variousfeatures that simplify the developer's task to create variousapplications. One such application is SPLUNK® APP FOR VMWARE® thatprovides operational visibility into granular performance metrics, logs,tasks and events, and topology from hosts, virtual machines and virtualcenters. It empowers administrators with an accurate real-time pictureof the health of the environment, proactively identifying performanceand capacity bottlenecks.

Conventional data-center-monitoring systems lack the infrastructure toeffectively store and analyze large volumes of machine-generated data,such as performance information and log data obtained from the datacenter. In conventional data-center-monitoring systems,machine-generated data is typically pre-processed prior to being stored,for example, by extracting pre-specified data items and storing them ina database to facilitate subsequent retrieval and analysis at searchtime. However, the rest of the data is not saved and discarded duringpre-processing.

In contrast, the SPLUNK® APP FOR VMWARE® stores large volumes ofminimally processed machine data, such as performance information andlog data, at ingestion time for later retrieval and analysis at searchtime when a live performance issue is being investigated. In addition todata obtained from various log files, this performance-relatedinformation can include values for performance metrics obtained throughan application programming interface (API) provided as part of thevSphere Hypervisor™ system distributed by VMware, Inc. of Palo Alto,Calif. For example, these performance metrics can include: (1)CPU-related performance metrics; (2) disk-related performance metrics;(3) memory-related performance metrics; (4) network-related performancemetrics; (5) energy-usage statistics; (6) data-traffic-relatedperformance metrics; (7) overall system availability performancemetrics; (8) cluster-related performance metrics; and (9) virtualmachine performance statistics. Such performance metrics are describedin U.S. patent application Ser. No. 14/167,316, entitled “CORRELATIONFOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCE METRICS OFCOMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROMTHAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

To facilitate retrieving information of interest from performance dataand log files, the SPLUNK® APP FOR VMWARE® provides pre-specifiedschemas for extracting relevant values from different types ofperformance-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR VMWARE® additionally provides various visualizationsto facilitate detecting and diagnosing the root cause of performanceproblems. For example, one such visualization is a “proactive monitoringtree” that enables a user to easily view and understand relationshipsamong various factors that affect the performance of a hierarchicallystructured computing system. This proactive monitoring tree enables auser to easily navigate the hierarchy by selectively expanding nodesrepresenting various entities (e.g., virtual centers or computingclusters) to view performance information for lower-level nodesassociated with lower-level entities (e.g., virtual machines or hostsystems). Example node-expansion operations are illustrated in FIG. 9C,wherein nodes 933 and 934 are selectively expanded. Note that nodes931-939 can be displayed using different patterns or colors to representdifferent performance states, such as a critical state, a warning state,a normal state or an unknown/offline state. The ease of navigationprovided by selective expansion in combination with the associatedperformance-state information enables a user to quickly diagnose theroot cause of a performance problem. The proactive monitoring tree isdescribed in further detail in U.S. patent application Ser. No.14/253,490, entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 15 Apr. 2014, and U.S. patent application Ser. No.14/812,948, also entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 29 Jul. 2015, each of which is hereby incorporated byreference in its entirety for all purposes.

The SPLUNK® APP FOR VMWARE® also provides a user interface that enablesa user to select a specific time range and then view heterogeneous datacomprising events, log data, and associated performance metrics for theselected time range. For example, the screen illustrated in FIG. 9Ddisplays a listing of recent “tasks and events” and a listing of recent“log entries” for a selected time range above a performance-metric graphfor “average CPU core utilization” for the selected time range. Notethat a user is able to operate pull-down menus 942 to selectivelydisplay different performance metric graphs for the selected time range.This enables the user to correlate trends in the performance-metricgraph with corresponding event and log data to quickly determine theroot cause of a performance problem. This user interface is described inmore detail in U.S. patent application Ser. No. 14/167,316, entitled“CORRELATION FOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCEMETRICS OF COMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOGDATA FROM THAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan.2014, and which is hereby incorporated by reference in its entirety forall purposes.

2.13. Cloud-Based System Overview

The example data intake and query system 108 described in reference toFIG. 2 comprises several system components, including one or moreforwarders, indexers, and search heads. In some environments, a user ofa data intake and query system 108 may install and configure, oncomputing devices owned and operated by the user, one or more softwareapplications that implement some or all of these system components. Forexample, a user may install a software application on server computersowned by the user and configure each server to operate as one or more ofa forwarder, an indexer, a search head, etc. This arrangement generallymay be referred to as an “on-premises” solution. That is, the system 108is installed and operates on computing devices directly controlled bythe user of the system. Some users may prefer an on-premises solutionbecause it may provide a greater level of control over the configurationof certain aspects of the system (e.g., security, privacy, standards,controls, etc.). However, other users may instead prefer an arrangementin which the user is not directly responsible for providing and managingthe computing devices upon which various components of system 108operate.

In one embodiment, to provide an alternative to an entirely on-premisesenvironment for system 108, one or more of the components of a dataintake and query system instead may be provided as a cloud-basedservice. In this context, a cloud-based service refers to a servicehosted by one more computing resources that are accessible to end usersover a network, for example, by using a web browser or other applicationon a client device to interface with the remote computing resources. Forexample, a service provider may provide a cloud-based data intake andquery system by managing computing resources configured to implementvarious aspects of the system (e.g., forwarders, indexers, search heads,etc.) and by providing access to the system to end users via a network.Typically, a user may pay a subscription or other fee to use such aservice. Each subscribing user of the cloud-based service may beprovided with an account that enables the user to configure a customizedcloud-based system based on the user's preferences.

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system. Similar to the system of FIG. 2, the networkedcomputer system 1000 includes input data sources 202 and forwarders 204.These input data sources and forwarders may be in a subscriber's privatecomputing environment. Alternatively, they might be directly managed bythe service provider as part of the cloud service. In the example system1000, one or more forwarders 204 and client devices 1002 are coupled toa cloud-based data intake and query system 1006 via one or more networks1004. Network 1004 broadly represents one or more LANs, WANs, cellularnetworks, intranetworks, internetworks, etc., using any of wired,wireless, terrestrial microwave, satellite links, etc., and may includethe public Internet, and is used by client devices 1002 and forwarders204 to access the system 1006. Similar to the system of 108, each of theforwarders 204 may be configured to receive data from an input sourceand to forward the data to other components of the system 1006 forfurther processing.

In an embodiment, a cloud-based data intake and query system 1006 maycomprise a plurality of system instances 1008. In general, each systeminstance 1008 may include one or more computing resources managed by aprovider of the cloud-based system 1006 made available to a particularsubscriber. The computing resources comprising a system instance 1008may, for example, include one or more servers or other devicesconfigured to implement one or more forwarders, indexers, search heads,and other components of a data intake and query system, similar tosystem 108. As indicated above, a subscriber may use a web browser orother application of a client device 1002 to access a web portal orother interface that enables the subscriber to configure an instance1008.

Providing a data intake and query system as described in reference tosystem 108 as a cloud-based service presents a number of challenges.Each of the components of a system 108 (e.g., forwarders, indexers andsearch heads) may at times refer to various configuration files storedlocally at each component. These configuration files typically mayinvolve some level of user configuration to accommodate particular typesof data a user desires to analyze and to account for other userpreferences. However, in a cloud-based service context, users typicallymay not have direct access to the underlying computing resourcesimplementing the various system components (e.g., the computingresources comprising each system instance 1008) and may desire to makesuch configurations indirectly, for example, using one or more web-basedinterfaces. Thus, the techniques and systems described herein forproviding user interfaces that enable a user to configure source typedefinitions are applicable to both on-premises and cloud-based servicecontexts, or some combination thereof (e.g., a hybrid system where bothan on-premises environment such as SPLUNK® ENTERPRISE and a cloud-basedenvironment such as SPLUNK CLOUD are centrally visible).

2.14. Searching Externally Archived Data

FIG. 11 shows a block diagram of an example of a data intake and querysystem 108 that provides transparent search facilities for data systemsthat are external to the data intake and query system. Such facilitiesare available in the HUNK® system provided by Splunk Inc. of SanFrancisco, Calif. HUNK® represents an analytics platform that enablesbusiness and IT teams to rapidly explore, analyze, and visualize data inHadoop and NoSQL data stores.

The search head 210 of the data intake and query system receives searchrequests from one or more client devices 1104 over network connections1120. As discussed above, the data intake and query system 108 mayreside in an enterprise location, in the cloud, etc. FIG. 11 illustratesthat multiple client devices 1104 a, 1104 b, . . . , 1104 n maycommunicate with the data intake and query system 108. The clientdevices 1104 may communicate with the data intake and query system usinga variety of connections. For example, one client device in FIG. 11 isillustrated as communicating over an Internet (Web) protocol, anotherclient device is illustrated as communicating via a command lineinterface, and another client device is illustrated as communicating viaa system developer kit (SDK).

The search head 210 analyzes the received search request to identifyrequest parameters. If a search request received from one of the clientdevices 1104 references an index maintained by the data intake and querysystem, then the search head 210 connects to one or more indexers 206 ofthe data intake and query system for the index referenced in the requestparameters. That is, if the request parameters of the search requestreference an index, then the search head accesses the data in the indexvia the indexer. The data intake and query system 108 may include one ormore indexers 206, depending on system access resources andrequirements. As described further below, the indexers 206 retrieve datafrom their respective local data stores 208 as specified in the searchrequest. The indexers and their respective data stores can comprise oneor more storage devices and typically reside on the same system, thoughthey may be connected via a local network connection.

If the request parameters of the received search request reference anexternal data collection, which is not accessible to the indexers 206 orunder the management of the data intake and query system, then thesearch head 210 can access the external data collection through anExternal Result Provider (ERP) process 1110. An external data collectionmay be referred to as a “virtual index” (plural, “virtual indices”). AnERP process provides an interface through which the search head 210 mayaccess virtual indices.

Thus, a search reference to an index of the system relates to a locallystored and managed data collection. In contrast, a search reference to avirtual index relates to an externally stored and managed datacollection, which the search head may access through one or more ERPprocesses 1110, 1112. FIG. 11 shows two ERP processes 1110, 1112 thatconnect to respective remote (external) virtual indices, which areindicated as a Hadoop or another system 1114 (e.g., Amazon S3, AmazonEMR, other Hadoop Compatible File Systems (HCFS), etc.) and a relationaldatabase management system (RDBMS) 1116. Other virtual indices mayinclude other file organizations and protocols, such as Structured QueryLanguage (SQL) and the like. The ellipses between the ERP processes1110, 1112 indicate optional additional ERP processes of the data intakeand query system 108. An ERP process may be a computer process that isinitiated or spawned by the search head 210 and is executed by thesearch data intake and query system 108. Alternatively or additionally,an ERP process may be a process spawned by the search head 210 on thesame or different host system as the search head 210 resides.

The search head 210 may spawn a single ERP process in response tomultiple virtual indices referenced in a search request, or the searchhead may spawn different ERP processes for different virtual indices.Generally, virtual indices that share common data configurations orprotocols may share ERP processes. For example, all search queryreferences to a Hadoop file system may be processed by the same ERPprocess, if the ERP process is suitably configured. Likewise, all searchquery references to an SQL database may be processed by the same ERPprocess. In addition, the search head may provide a common ERP processfor common external data source types (e.g., a common vendor may utilizea common ERP process, even if the vendor includes different data storagesystem types, such as Hadoop and SQL). Common indexing schemes also maybe handled by common ERP processes, such as flat text files or Weblogfiles.

The search head 210 determines the number of ERP processes to beinitiated via the use of configuration parameters that are included in asearch request message. Generally, there is a one-to-many relationshipbetween an external results provider “family” and ERP processes. Thereis also a one-to-many relationship between an ERP process andcorresponding virtual indices that are referred to in a search request.For example, using RDBMS, assume two independent instances of such asystem by one vendor, such as one RDBMS for production and another RDBMSused for development. In such a situation, it is likely preferable (butoptional) to use two ERP processes to maintain the independent operationas between production and development data. Both of the ERPs, however,will belong to the same family, because the two RDBMS system types arefrom the same vendor.

The ERP processes 1110, 1112 receive a search request from the searchhead 210. The search head may optimize the received search request forexecution at the respective external virtual index. Alternatively, theERP process may receive a search request as a result of analysisperformed by the search head or by a different system process. The ERPprocesses 1110, 1112 can communicate with the search head 210 viaconventional input/output routines (e.g., standard in/standard out,etc.). In this way, the ERP process receives the search request from aclient device such that the search request may be efficiently executedat the corresponding external virtual index.

The ERP processes 1110, 1112 may be implemented as a process of the dataintake and query system. Each ERP process may be provided by the dataintake and query system, or may be provided by process or applicationproviders who are independent of the data intake and query system. Eachrespective ERP process may include an interface application installed ata computer of the external result provider that ensures propercommunication between the search support system and the external resultprovider. The ERP processes 1110, 1112 generate appropriate searchrequests in the protocol and syntax of the respective virtual indices1114, 1116, each of which corresponds to the search request received bythe search head 210. Upon receiving search results from theircorresponding virtual indices, the respective ERP process passes theresult to the search head 210, which may return or display the resultsor a processed set of results based on the returned results to therespective client device.

Client devices 1104 may communicate with the data intake and querysystem 108 through a network interface 1120, e.g., one or more LANs,WANs, cellular networks, intranetworks, and/or internetworks using anyof wired, wireless, terrestrial microwave, satellite links, etc., andmay include the public Internet.

The analytics platform utilizing the External Result Provider processdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “EXTERNALRESULT PROVIDED PROCESS FOR RETRIEVING DATA STORED USING A DIFFERENTCONFIGURATION OR PROTOCOL”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSINGA SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filedon 1 May 2014, and U.S. patent application Ser. No. 14/449,144, entitled“PROCESSING A SYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”,filed on 31 Jul. 2014, each of which is hereby incorporated by referencein its entirety for all purposes.

2.14.1. ERP Process Features

The ERP processes described above may include two operation modes: astreaming mode and a reporting mode. The ERP processes can operate instreaming mode only, in reporting mode only, or in both modessimultaneously. Operating in both modes simultaneously is referred to asmixed mode operation. In a mixed mode operation, the ERP at some pointcan stop providing the search head with streaming results and onlyprovide reporting results thereafter, or the search head at some pointmay start ignoring streaming results it has been using and only usereporting results thereafter.

The streaming mode returns search results in real time, with minimalprocessing, in response to the search request. The reporting modeprovides results of a search request with processing of the searchresults prior to providing them to the requesting search head, which inturn provides results to the requesting client device. ERP operationwith such multiple modes provides greater performance flexibility withregard to report time, search latency, and resource utilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the raw dataobtained from the external data source) are provided to the search head,which can then process the results data (e.g., break the raw data intoevents, timestamp it, filter it, etc.) and integrate the results datawith the results data from other external data sources, and/or from datastores of the search head. The search head performs such processing andcan immediately start returning interim (streaming mode) results to theuser at the requesting client device; simultaneously, the search head iswaiting for the ERP process to process the data it is retrieving fromthe external data source as a result of the concurrently executingreporting mode.

In some instances, the ERP process initially operates in a mixed mode,such that the streaming mode operates to enable the ERP quickly toreturn interim results (e.g., some of the raw or unprocessed datanecessary to respond to a search request) to the search head, enablingthe search head to process the interim results and begin providing tothe client or search requester interim results that are responsive tothe query. Meanwhile, in this mixed mode, the ERP also operatesconcurrently in reporting mode, processing portions of raw data in amanner responsive to the search query. Upon determining that it hasresults from the reporting mode available to return to the search head,the ERP may halt processing in the mixed mode at that time (or somelater time) by stopping the return of data in streaming mode to thesearch head and switching to reporting mode only. The ERP at this pointstarts sending interim results in reporting mode to the search head,which in turn may then present this processed data responsive to thesearch request to the client or search requester. Typically the searchhead switches from using results from the ERP's streaming mode ofoperation to results from the ERP's reporting mode of operation when thehigher bandwidth results from the reporting mode outstrip the amount ofdata processed by the search head in the streaming mode of ERPoperation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head for processingall the raw data. In addition, the ERP may optionally direct anotherprocessor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head couldsimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, a variety of triggers and ways to accomplish asearch head's switch from using streaming mode results to usingreporting mode results may be appreciated by one skilled in the art.

The reporting mode can involve the ERP process (or an external system)performing event breaking, time stamping, filtering of events to matchthe search query request, and calculating statistics on the results. Theuser can request particular types of data, such as if the search queryitself involves types of events, or the search request may ask forstatistics on data, such as on events that meet the search request. Ineither case, the search head understands the query language used in thereceived query request, which may be a proprietary language. Oneexemplary query language is Splunk Processing Language (SPL) developedby the assignee of the application, Splunk Inc. The search headtypically understands how to use that language to obtain data from theindexers, which store data in a format used by the SPLUNK® Enterprisesystem.

The ERP processes support the search head, as the search head is notordinarily configured to understand the format in which data is storedin external data sources such as Hadoop or SQL data systems. Rather, theERP process performs that translation from the query submitted in thesearch support system's native format (e.g., SPL if SPLUNK® ENTERPRISEis used as the search support system) to a search query request formatthat will be accepted by the corresponding external data system. Theexternal data system typically stores data in a different format fromthat of the search support system's native index format, and it utilizesa different query language (e.g., SQL or MapReduce, rather than SPL orthe like).

As noted, the ERP process can operate in the streaming mode alone. Afterthe ERP process has performed the translation of the query request andreceived raw results from the streaming mode, the search head canintegrate the returned data with any data obtained from local datasources (e.g., native to the search support system), other external datasources, and other ERP processes (if such operations were required tosatisfy the terms of the search query). An advantage of mixed modeoperation is that, in addition to streaming mode, the ERP process isalso executing concurrently in reporting mode. Thus, the ERP process(rather than the search head) is processing query results (e.g.,performing event breaking, timestamping, filtering, possibly calculatingstatistics if required to be responsive to the search query request,etc.). It should be apparent to those skilled in the art that additionaltime is needed for the ERP process to perform the processing in such aconfiguration. Therefore, the streaming mode will allow the search headto start returning interim results to the user at the client devicebefore the ERP process can complete sufficient processing to startreturning any search results. The switchover between streaming andreporting mode happens when the ERP process determines that theswitchover is appropriate, such as when the ERP process determines itcan begin returning meaningful results from its reporting mode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results can be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults can be processed per unit of time). For example, when the ERPprocess does begin returning report results, it returns more processedresults than in the streaming mode, because, e.g., statistics only needto be calculated to be responsive to the search request. That is, theERP process doesn't have to take time to first return raw data to thesearch head. As noted, the ERP process could be configured to operate instreaming mode alone and return just the raw data for the search head toprocess in a way that is responsive to the search request.Alternatively, the ERP process can be configured to operate in thereporting mode only. Also, the ERP process can be configured to operatein streaming mode and reporting mode concurrently, as described, withthe ERP process stopping the transmission of streaming results to thesearch head when the concurrently running reporting mode has caught upand started providing results. The reporting mode does not require theprocessing of all raw data that is responsive to the search queryrequest before the ERP process starts returning results; rather, thereporting mode usually performs processing of chunks of events andreturns the processing results to the search head for each chunk.

For example, an ERP process can be configured to merely return thecontents of a search result file verbatim, with little or no processingof results. That way, the search head performs all processing (such asparsing byte streams into events, filtering, etc.). The ERP process canbe configured to perform additional intelligence, such as analyzing thesearch request and handling all the computation that a native searchindexer process would otherwise perform. In this way, the configured ERPprocess provides greater flexibility in features while operatingaccording to desired preferences, such as response latency and resourcerequirements.

2.14. IT Service Monitoring

As previously mentioned, the SPLUNK® ENTERPRISE platform providesvarious schemas, dashboards and visualizations that make it easy fordevelopers to create applications to provide additional capabilities.One such application is SPLUNK® IT SERVICE INTELLIGENCE™, which performsmonitoring and alerting operations. It also includes analytics to helpan analyst diagnose the root cause of performance problems based onlarge volumes of data stored by the SPLUNK® ENTERPRISE system ascorrelated to the various services an IT organization provides (aservice-centric view). This differs significantly from conventional ITmonitoring systems that lack the infrastructure to effectively store andanalyze large volumes of service-related event data. Traditional servicemonitoring systems typically use fixed schemas to extract data frompre-defined fields at data ingestion time, wherein the extracted data istypically stored in a relational database. This data extraction processand associated reduction in data content that occurs at data ingestiontime inevitably hampers future investigations, when all of the originaldata may be needed to determine the root cause of or contributingfactors to a service issue.

In contrast, a SPLUNK® IT SERVICE INTELLIGENCE™ system stores largevolumes of minimally-processed service-related data at ingestion timefor later retrieval and analysis at search time, to perform regularmonitoring, or to investigate a service issue. To facilitate this dataretrieval process, SPLUNK® IT SERVICE INTELLIGENCE™ enables a user todefine an IT operations infrastructure from the perspective of theservices it provides. In this service-centric approach, a service suchas corporate e-mail may be defined in terms of the entities employed toprovide the service, such as host machines and network devices. Eachentity is defined to include information for identifying all of theevent data that pertains to the entity, whether produced by the entityitself or by another machine, and considering the many various ways theentity may be identified in raw machine data (such as by a URL, an IPaddress, or machine name). The service and entity definitions canorganize event data around a service so that all of the event datapertaining to that service can be easily identified. This capabilityprovides a foundation for the implementation of Key PerformanceIndicators.

One or more Key Performance Indicators (KPI's) are defined for a servicewithin the SPLUNK® IT SERVICE INTELLIGENCE™ application. Each KPImeasures an aspect of service performance at a point in time or over aperiod of time (aspect KPI's). Each KPI is defined by a search querythat derives a KPI value from the machine data of events associated withthe entities that provide the service. Information in the entitydefinitions may be used to identify the appropriate events at the time aKPI is defined or whenever a KPI value is being determined. The KPIvalues derived over time may be stored to build a valuable repository ofcurrent and historical performance information for the service, and therepository, itself, may be subject to search query processing. AggregateKPIs may be defined to provide a measure of service performancecalculated from a set of service aspect KPI values; this aggregate mayeven be taken across defined timeframes and/or across multiple services.A particular service may have an aggregate KPI derived fromsubstantially all of the aspect KPI's of the service to indicate anoverall health score for the service.

SPLUNK® IT SERVICE INTELLIGENCE™ facilitates the production ofmeaningful aggregate KPI's through a system of KPI thresholds and statevalues. Different KPI definitions may produce values in differentranges, and so the same value may mean something very different from oneKPI definition to another. To address this, SPLUNK® IT SERVICEINTELLIGENCE™ implements a translation of individual KPI values to acommon domain of “state” values. For example, a KPI range of values maybe 1-100, or 50-275, while values in the state domain may be ‘critical,’‘warning,’ ‘normal,’ and ‘informational’. Thresholds associated with aparticular KPI definition determine ranges of values for that KPI thatcorrespond to the various state values. In one case, KPI values 95-100may be set to correspond to ‘critical’ in the state domain. KPI valuesfrom disparate KPI's can be processed uniformly once they are translatedinto the common state values using the thresholds. For example, “normal80% of the time” can be applied across various KPI's. To providemeaningful aggregate KPI's, a weighting value can be assigned to eachKPI so that its influence on the calculated aggregate KPI value isincreased or decreased relative to the other KPI's.

One service in an IT environment often impacts, or is impacted by,another service. SPLUNK® IT SERVICE INTELLIGENCE™ can reflect thesedependencies. For example, a dependency relationship between a corporatee-mail service and a centralized authentication service can be reflectedby recording an association between their respective servicedefinitions. The recorded associations establish a service dependencytopology that informs the data or selection options presented in a GUI,for example. (The service dependency topology is like a “map” showinghow services are connected based on their dependencies.) The servicetopology may itself be depicted in a GUI and may be interactive to allownavigation among related services.

Entity definitions in SPLUNK® IT SERVICE INTELLIGENCE™ can includeinformational fields that can serve as metadata, implied data fields, orattributed data fields for the events identified by other aspects of theentity definition. Entity definitions in SPLUNK® IT SERVICEINTELLIGENCE™ can also be created and updated by an import of tabulardata (as represented in a CSV, another delimited file, or a search queryresult set). The import may be GUI-mediated or processed using importparameters from a GUI-based import definition process. Entitydefinitions in SPLUNK® IT SERVICE INTELLIGENCE™ can also be associatedwith a service by means of a service definition rule. Processing therule results in the matching entity definitions being associated withthe service definition. The rule can be processed at creation time, andthereafter on a scheduled or on-demand basis. This allows dynamic,rule-based updates to the service definition.

During operation, SPLUNK® IT SERVICE INTELLIGENCE™ can recognizeso-called “notable events” that may indicate a service performanceproblem or other situation of interest. These notable events can berecognized by a “correlation search” specifying trigger criteria for anotable event: every time KPI values satisfy the criteria, theapplication indicates a notable event. A severity level for the notableevent may also be specified. Furthermore, when trigger criteria aresatisfied, the correlation search may additionally or alternativelycause a service ticket to be created in an IT service management (ITSM)system, such as a systems available from ServiceNow, Inc., of SantaClara, Calif.

SPLUNK® IT SERVICE INTELLIGENCE™ provides various visualizations builton its service-centric organization of event data and the KPI valuesgenerated and collected. Visualizations can be particularly useful formonitoring or investigating service performance. SPLUNK® IT SERVICEINTELLIGENCE™ provides a service monitoring interface suitable as thehome page for ongoing IT service monitoring. The interface isappropriate for settings such as desktop use or for a wall-mounteddisplay in a network operations center (NOC). The interface mayprominently display a services health section with tiles for theaggregate KPI's indicating overall health for defined services and ageneral KPI section with tiles for KPI's related to individual serviceaspects. These tiles may display KPI information in a variety of ways,such as by being colored and ordered according to factors like the KPIstate value. They also can be interactive and navigate to visualizationsof more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a service-monitoring dashboardvisualization based on a user-defined template. The template can includeuser-selectable widgets of varying types and styles to display KPIinformation. The content and the appearance of widgets can responddynamically to changing KPI information. The KPI widgets can appear inconjunction with a background image, user drawing objects, or othervisual elements, that depict the IT operations environment, for example.The KPI widgets or other GUI elements can be interactive so as toprovide navigation to visualizations of more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization showingdetailed time-series information for multiple KPI's in parallel graphlanes. The length of each lane can correspond to a uniform time range,while the width of each lane may be automatically adjusted to fit thedisplayed KPI data. Data within each lane may be displayed in a userselectable style, such as a line, area, or bar chart. During operation auser may select a position in the time range of the graph lanes toactivate lane inspection at that point in time. Lane inspection maydisplay an indicator for the selected time across the graph lanes anddisplay the KPI value associated with that point in time for each of thegraph lanes. The visualization may also provide navigation to aninterface for defining a correlation search, using information from thevisualization to pre-populate the definition.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization for incidentreview showing detailed information for notable events. The incidentreview visualization may also show summary information for the notableevents over a time frame, such as an indication of the number of notableevents at each of a number of severity levels. The severity leveldisplay may be presented as a rainbow chart with the warmest colorassociated with the highest severity classification. The incident reviewvisualization may also show summary information for the notable eventsover a time frame, such as the number of notable events occurring withinsegments of the time frame. The incident review visualization maydisplay a list of notable events within the time frame ordered by anynumber of factors, such as time or severity. The selection of aparticular notable event from the list may display detailed informationabout that notable event, including an identification of the correlationsearch that generated the notable event.

SPLUNK® IT SERVICE INTELLIGENCE™ provides pre-specified schemas forextracting relevant values from the different types of service-relatedevent data. It also enables a user to define such schemas.

2.15 Cloud-Based Architecture

As shown in the previous figures, various embodiments may refer to adata intake and query system 108 that includes one or more of a searchhead 210, an indexer 206, and a forwarder 204. In other implementations,data intake and query system 108 may have a different architecture, butmay carry out indexing and searching in a way that is indistinguishableor functionally equivalent from the perspective of the end user. Forexample, data intake and query system 108 may be re-architected to runin a stateless, containerized environment. In some of these embodiments,data intake and query system 108 may be run in a computing cloudprovided by a third party, or provided by the operator of the dataintake and query system 108. This type of cloud-based data intake andquery system may have several benefits, including, but not limited to,lossless data ingestion, more robust disaster recovery, and faster ormore efficient processing, searching, and indexing. A cloud-based dataintake and query system as described in this section may provideseparately scalable storage resources and compute resources, orseparately scalable search and index resources. Additionally, thecloud-based data intake and query system may allow for applications tobe developed on top of the data intake and query system, to extend orenhance functionality, through a gateway layer or one or moreApplication Programming Interfaces (APIs), which may providecustomizable access control or targeted exposure to the workings of dataintake and query system 108.

In some embodiments, a cloud-based data intake and query system mayinclude an intake system. Such an intake system can include, but is notlimited to an intake buffer, such as Apache Kafka® or Amazon Kinesis®,or an extensible compute layer, such as Apache Spark™ or Apache Flink®.In some embodiments, the search function and the index function may beseparated or containerized, so that search functions and index functionsmay run or scale independently. In some embodiments, data that isindexed may be stored in buckets, which may be stored in a persistentstorage once certain bucket requirements have been met, and retrieved asneeded for searching. In some embodiments, the search functions andindex functions run in stateless containers, which may be coordinated byan orchestration platform. These containerized search and indexfunctions may retrieve data needed to carry out searching and indexingfrom the buckets or various other services that may also run incontainers, or within other components of the orchestration platform. Inthis manner, loss of a single container, or even multiple containers,does not result in data loss, because the data can be quickly recoveredfrom the various services or components or the buckets in which the datais persisted.

In some embodiments, the cloud-based data intake and query system mayimplement tenant-based and user-based access control. In someembodiments, the cloud-based data intake and query system may implementan abstraction layer, through a gateway portal, an API, or somecombination thereof, to control or limit access to the functionality ofthe cloud-based data intake and query system.

3.0 Overview of Facilitating Scalable and Secure Data Collection

Collecting data is important for performing various types of analyses.For example, collected data, such as machine-generated data (e.g.,performance data, diagnostic data, etc.), may be analyzed to diagnoseperformance problems, monitor user interactions, and to derive otherinsights. In many implementations, large-scale data collection is usedto collect extensive amounts of data. Oftentimes, however, it isdifficult to collect extensive amounts of data, particularly whencollecting data from multiple sources. In particular, collectingextensive amounts of data from multiple sources can be inefficientand/or insecure.

By way of example, a data-processing system may collect data frommultiple data sources, including data sources operated via differententities and/or platforms. To collect data from a data source, aconnector associated with the data source can communicate with the datasource to obtain or collect data. Oftentimes, a connector is responsiblefor collecting data from a particular data source. As such, to collectdata from multiple data sources, multiple connectors are used tocommunicate with the corresponding data source. As each data source maybe differently configured, generating connectors to communicate with anextensive amount of data sources (e.g., databases) can be difficult andtime consuming. In some cases, to enable connectors to collect data fromvarious data sources, it may be desirable for various entities togenerate connectors to collect data from particular data sources. Forexample, an entity (e.g., a third-party to a data collection system,such as a developer associated with a particular data source) mayeffectively and efficiently generate or create code for a connector tocommunicate with a particular data source to collect data.

In existing implementations that perform large-scale data collection ina containerized orchestration platform, data-collection execution (e.g.,run time) functionality generally operates via a single pod orcontainer. In this regard, connector functionality is performed in asame pod or container in which other functionality is performed. Forinstance, communication with other resources of a data-collection system(e.g., a task queue, such as task queue 1970 of FIG. 19 (SQS taskqueue), a pipeline stream (Kinesis), a data processing system, etc.) canbe executed within a same pod or container as connector functionality.As such, permissions or authorization provided to perform connectorfunctionality might result in undesired access to such resources. Accessto restricted resources may be particularly undesired in cases in whichthird-party entities create connector code.

Further, as data-collection execution (e.g., run time) functionalitygenerally operates via a single container or pod in priorimplementations, data collection operates in a single-threaded manner.Using a single pod or container to perform data collection in asingle-threaded manner limits scalability of data collection, therebylimiting the efficiency of performing data collection, particularly in alarge-scale manner.

Accordingly, embodiments of the present disclosure are directed tofacilitating scalable and secure data collection. In particular,embodiments described herein enable scalability of data collection in asecure manner by, among other things, abstracting a connector(s) to apod(s) and/or container(s) that executes separate from otherdata-collecting functionality. This abstraction or separation ofconnector functionality from other data-collecting functionalityprevents access to restricted resources in connection with adata-collection system that may otherwise be provided via permissions orauthorizations provided to perform connector functionality. Such accessprevention is particularly valuable when the connector functionality isgenerated or created by a third-party to the data collection system(e.g., third-party developers creating code to enable access to data ofa third-party data source). For example, a malicious third-partydeveloper can generate code designed to attempt exfiltration of datafrom the data-collection system, or otherwise hinder the performance ofthe data-collection system. Similarly, a benign third-party developercould generate code that, while not intentionally malicious, introducessecurity vulnerabilities, or interacts with the data-collection systemin a manner that causes performance of the data-collection system tosuffer. Additionally, this reduces drag on the third-party (orfirst-party) developers who are building the connectors, in that theymay not be required to build their connector or other application withlarge-scale scalability in mind. In this way, embodiments describedherein provide enhanced security via a data-collection system enablingscalability in generating connector functionality (e.g., via thirdparties), thereby enhancing the scalability of the data-collectionsystem. Further, such embodiments allow existing connectors, which mayhave been built without concern for data collection on a large scale, tobe used in their current form, without requiring rewriting.

In addition to enhancing scalability by enabling utilization ofthird-party created connector functionality, the abstraction ofconnector functionality from other data-collecting functionality enablesscalability in run-time execution of data collection. In particular, andas discussed more fully below, utilized resources can be monitored suchthat additional pods and/or connectors may be deployed, as needed, toscale execution of data collection.

In some implementations, to enhance efficiency of data collection, thedata-collection system may use a discover process that discovers data tocollect and a collect process that collects data. Implementing variouscomponents using separate processes enables both processes to execute inparallel or concurrently, at least partially, to facilitate efficiencyof data collection. For example, as a first set of data to collect isdiscovered via the discover process, an indication of the first set ofdata can be provided to the collect process to begin collecting thefirst set of data. The discover process may continue executing todiscover or identify other sets of data to collect and, as discovered,provide an indication of such identified data sets to the collectprocess for collecting the data.

3.1 OVERVIEW OF A SCALABLE DATA-EXCHANGE SYSTEM IN A DATA-EXCHANGEENVIRONMENT

FIG. 18 illustrates an example data-exchange environment 1800 inaccordance with various embodiments of the present disclosure.Generally, the data-exchange environment 1800 refers to an environmentthat provides for, or enables, the management, storage, retrieval,collection (input), and/or exportation (output) of data. As shown inFIG. 18, the data-exchange environment includes a data-exchange system1802 used to facilitate enhancement of data exchange such that data canbe exchanged in a scalable and secure manner. Many embodiments describedherein discuss the data-exchange system operating to facilitate datacollection. In this regard, the data-exchange system is used to collectdata in a scalable and secure manner. As can be appreciated, however,the data-exchange system can additionally or alternatively be used toperform other data exchange, such as exporting or outputting data.Further, use of the term “exchange” herein can refer to collecting data,exporting data, and/or both collecting and exporting data.

In some embodiments, the environment 1800 can include a data-exchangesystem 1802 communicatively coupled to one or more client devices 1804and one or more data sources 1806 via a communications network 1808. Thenetwork 1808 may include an element or system that facilitatescommunication between the entities of the environment 1800. The network1808 may include an electronic communications network, such as theInternet, a local area network (LAN), a wide area network (WAN), awireless local area network (WLAN), a cellular communications network,and/or the like. In some embodiments, the network 1808 can include awired or a wireless network. In some embodiments, the network 1808 caninclude a single network or a combination of networks.

The data source(s) 1806 may be a source of data available for collectingvia the data-exchange system 1802. A data source 1806 can be or includeone or more external data sources, such as web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,and/or the like. Data source 1806 may be located remote from thedata-exchange system 1802. For example, a data source 1806 may bedefined on an agent computer operating remote from the data-exchangesystem 1802, such as on-site at a customer's location, that communicatesdata to data-exchange system 1802 via a communications network (e.g.,network 1808). As can be appreciated, data may be collected from orexported to a variety of data sources, including data sources managed bydifferent entities. For instance, a first data source may be managed bya first entity and a second data source managed by a second entity.Further, data sources may be operated via different platforms.

Data can be a stream or set of data fed to a component of thedata-exchange system 1802, such as a connector, as described in moredetail below. In some embodiments, the data may be heterogeneousmachine-generated data from various data sources 1806, such as servers,databases, applications, networks, and/or the like. Data may include,for example raw data, such as server log files, activity log files,configuration files, messages, network packet data, performancemeasurements, sensor measurements, and/or the like. For example, datamay include log data generated by a server during the normal course ofoperation (e.g. server log data).

As can be appreciated, data might be structured data or unstructureddata. Structured data has a predefined format, wherein specific dataitems with specific data formats reside at predefined locations in thedata. For example, data contained in relational databases andspreadsheets may be structured data sets. In contrast, unstructured datadoes not have a predefined format. This means that unstructured data cancomprise various data items having different data types that can resideat different locations.

The data-exchange system 1802 is generally configured to collect orexport data from or to the data source(s) 1806. In accordance withcollecting data from a data source, the data-exchange system 1802 canprovide such collected data to a data-processing system 1810. Adata-processing system 1810 can provide for, or enable, the management,storage, retrieval, processing, and/or analysis of data. Althoughillustrated as providing collected data to a data-processing system1810, the data can be collected and provided to any number of systems,servers, components, message bus, etc. and is not intended to be limitedherein. For instance, data may be provided to a pipeline stream thatqueues data for a data-processing system 1810, such as a data intake andquery system (e.g., data intake and query system 108 of FIG. 1).

One exemplary data-processing system 1810 may generate events used fordata analysis. In one implementation, a data-processing system 1810 mayinclude forwarders, indexers, and data stores. For example, a forwardermay obtain data and provide the data to an indexer for indexing. Anindexer of the data-processing system may obtain the data and apportionthe data into events. Generally, an indexer may be an entity of thedata-processing system that indexes data, transforms data into events,and places the results into a data store or index. An indexer 1812 mayperform other functions, such as data input and search management.

During indexing, and at a high-level, the indexer can facilitate takingdata from its origin in sources, such as log files and network feeds, toits transformation into searchable events that encapsulate valuableknowledge. The indexer may acquire a raw data stream (e.g., data), forexample via the data-exchange system 1802, break it into blocks (e.g.,64K blocks of data), and/or annotate each block with metadata keys.After the data has been input, the data can be parsed. This can include,for example, identifying event boundaries, identifying event timestamps(or creating them if they don't exist), applying custom metadata toincoming events, and/or the like. Accordingly, the raw data may be databroken into individual events. As discussed, an event generally refersto a portion, or a segment of the data, that is associated with a time(e.g., via a timestamp). And, the resulting events may be indexed (e.g.,stored in a raw data file associated with an index file). In someembodiments, indexing the data may include additional processing, suchas compression, replication, and/or the like.

As can be appreciated, a data store(s) (e.g., a single data store ormultiple data stores, such as distributed data stores) of thedata-processing system 1810 may store the data (e.g., events) in anymanner. In some implementations, the data may include one or moreindexes including one or more buckets, and the buckets may include anindex file and/or raw data file (e.g., including parsed, timestampedevents). In some embodiments, each data store is managed by a givenindexer that stores data to the data store and/or performs searches ofthe data stored on the data store.

As described, events within the data store may be represented by a datastructure that is associated with a certain point in time and includes aportion of raw machine data (e.g., a portion of machine-generated datathat has not been manipulated). An event may include, for example, aline of data that includes a time reference (e.g., a timestamp), and oneor more other values. In the context of server log data, for example, anevent may correspond to a log entry for a client request and include thefollowing values: (a) a time value (e.g., including a value for the dateand time of the request, such as a timestamp), and (b) a series of othervalues including, for example, a page value (e.g., including a valuerepresenting the page requested), an IP (Internet Protocol) value (e.g.,including a value for representing the client IP address associated withthe request), and an HTTP (Hypertext Transfer protocol) code value(e.g., including a value representative of an HTTP status code), and/orthe like. That is, each event may be associated with one or more values.Some events may be associated with default values, such as a host value,a source value, a source type value and/or a time value. A default valuemay be common to some or all events of a set of source data.

In some embodiments, an event can be associated with one or morecharacteristics that are not represented by the data initially containedin the raw data, such as characteristics of the host, the source, and/orthe source type associated with the event. In the context of server logdata, for example, if an event corresponds to a log entry received fromServer A, the host and the source of the event may be identified asServer A, and the source type may be determined to be “server.” In someembodiments, values representative of the characteristics may be addedto (or otherwise associated with) the event. In the context of serverlog data, for example, if an event is received from Server A, a hostvalue (e.g., including a value representative of Server A), a sourcevalue (e.g., including a value representative of Server A), and a sourcetype value (e.g., including a value representative of a “server”) may beappended to (or otherwise associated with) the corresponding event.

In some embodiments, events can correspond to data that is generated ona regular basis and/or in response to the occurrence of a givenactivity. In the context of server log data, for example, a server thatlogs activity every second may generate a log entry every second, andthe log entries may be stored as corresponding events of the data.Similarly, a server that logs data upon the occurrence of an error maygenerate a log entry each time an error occurs, and the log entries maybe stored as corresponding events of the data.

As described herein, the data-exchange system 1802, or portion thereof,can be initiated by a user of the client device 1804. The client device1804 may be used or otherwise accessed by a user, such as a systemadministrator or a customer. A client device 1804 may include anyvariety of electronic devices. In some embodiments, a client device 1804can include a device capable of communicating information via thenetwork 1808. A client device 1804 may include one or more computerdevices, such as a desktop computer, a server, a laptop computer, atablet computer, a wearable computer device, a personal digitalassistant (PDA), a smart phone, and/or the like. In some embodiments, aclient device 1804 may be a client of the data-exchange system 1802and/or data-processing system 1810. In some embodiments, a client device1804 can include various input/output (I/O) interfaces, such as adisplay (e.g., for displaying a graphical user interface (GUI), anaudible output user interface (e.g., a speaker), an audible input userinterface (e.g., a microphone), an image acquisition interface (e.g., acamera), a keyboard, a pointer/selection device (e.g., a mouse, atrackball, a touchpad, a touchscreen, a gesture capture or detectingdevice, or a stylus), and/or the like. In some embodiments, a clientdevice 1804 can include general computing components and/or embeddedsystems optimized with specific components for performing specifictasks. In some embodiments, a client device 1804 can includeprograms/applications that can be used to generate a request forcontent, to provide content, to render content, and/or to send and/orreceive requests to and/or from other devices via the network 1808. Forexample, a client device 1804 may include an Internet browserapplication that facilitates communication with the data-exchange system1802 via the network 1808. In some embodiments, a program, orapplication, of a client device 1804 can include program modules havingprogram instructions that are executable by a computer system to performsome or all of the functionality described herein with regard to atleast client device 1804. In some embodiments, a client device 1804 caninclude one or more computer systems similar to that of the computersystem 2300 described below with regard to at least FIG. 23.

Data exchange can be initiated or triggered at the client device 1804via a graphical user interface (GUI). In some embodiments, thedata-exchange system 1802 (or other system, such as the data-processingsystem 1810) can provide for the display of a GUI. Such a GUI can bedisplayed on a client device 1804, and can present information relatingto initiating data exchange, performing data exchange, and/or viewingresults or alerts associated with data exchange.

Data exchange be initiated in any number of ways. In one implementation,data exchange can be initiated at a client device (e.g., by a user). Asone example, a user may select an icon or other indicator tospecifically initiate data exchange. For example, assume a user desiresto perform data collection or data analysis. In such a case, a user mayselect to collect data, for example, from one or more data sources. Ascan be appreciated, and as more fully discussed below, a user may inputany type of information to facilitate initiation of data exchange.

The data-exchange system 1802 is generally configured to facilitate dataexchange. In particular, the data-exchange system 1802 performs dataexchange in a scalable and secure manner. As described herein, dataexchange can be performed to collect data (or export data), such asextensive amounts of data, from (or to) various sources. As described,data exchange performed via the data-exchange system 1802 can beinitiated or triggered in response to a query, user selection, and/orautomatically. As one example, a user may input or select (e.g., viaclient device 1804) a query to initiate data exchange. A query may be inany form and is not intended to be limited herein to a particular formatand/or content. A query may be provided, for example, to initiate dataexchange (e.g., data collection) in accordance with a specified manner.The query can trigger the data-exchange system 1802 to initiate dataexchange. As another example, data exchange may be automaticallyinitiated.

As described herein, data exchange can be performed in acontainer-managed environment in a manner that enables scalable andsecure data exchange, such as data collection. A container-managedenvironment, or containerized-orchestration platform, generally refersto an environment, platform, or tool used to deploy, manage, and/ornetwork container-based applications, systems, or workloads. In thisway, a container-managed environment can maximize use of hardwareresources, such as memory, storage I/O, and network bandwidth. Oneexample of a container-management system or environment is Kubernetes.Kubernetes, as well as other container managers, can provide high-levelabstractions for managing groups of containers.

At a high level, a container-managed environment (e.g., Kubernetes) canoperate in association with a set of nodes or machines, such as physicalmachines and/or virtual machines. In operation, a container-managedenvironment may use jobs, or objects (e.g., job objects), to create anextraction around a pod. A job, or object, can create and manage apod(s) executing a process or task. For example, a job or other objectmay create a pod to execute a process or task as well as track thecompletion of the process or task. In embodiments, a job may be part ofa separate deployment or separate logical or physical construct ornamespace within the same deployment.

A pod generally refers to a separately deployable unit of computeresources. In some contexts, the pod may be managed by an orchestrationplatform. In some contexts, a pod represents a single instance of anapplication running in an orchestration platform. In some contexts, apod may contain a single container. In other contexts, a pod may containmultiple containers. A pod, executing on a node, may be created andmanaged by a corresponding job. A pod can represent an instance of atask, process, or application. In this regard, a pod can perform (e.g.,via a container(s)), the operation or execution of a task, process,application, or workload. Pods can be created and/or terminated on nodesas needed to conform to a desired state (e.g., specified by a user).Generally, a pod may include a logical collection of one or morecontainers that can operate together. A container may refer to anapplication that includes all of its own dependencies, so that it canrun separately and reliably in various computing environments. In thisregard, a container refers to a self-contained separate deployable unitof compute resources. Containers of a pod may share common networkingand storage resources from a host node, as well as specifications thatdetermine how the containers run. To this end, containers may bescheduled together on the same host, share the same network namespace,and/or mount the same external storage (Volumes). Containers can includethe components (e.g., files, environment variables, dependencies, andlibraries) used to run desired software.

In operation, the data-exchange system 1802 may include a discoverprocess and a collect process. The discover process is generallyconfigured to discover a set of data to exchange (e.g., collect). Theexchange process is generally configured to exchange (e.g., collect) theidentified data. Advantageously, performing the discover process and theexchange process separately enables parallel execution of processesthereby improving data exchange efficiency. Upon exchange (e.g.,collecting) data from the data source(s) 1806 via the exchange process,the data can be provided to the data-processing system 1810. Althoughillustrated as providing data to the data-processing system 1810, as canbe appreciated, the data can be provided to other components or systems,such as a message bus or a pipeline stream (e.g., that provides the datavia a queue to a data-processing system, or portion thereof).

Turning to FIG. 19, to perform data exchange (e.g., data collection),the data-exchange system 1902 may include a data exchange service 1920,a supervisor 1922, a discover coordinator 1924, a connector 1926, a dataexchange coordinator 1928, and a connector 1930. According toembodiments, the data-exchange system 1902 can include any number ofother components not illustrated. In some embodiments, one or more ofthe illustrated components 1920, 1922, 1924, 1926, 1928, and 1930 can beintegrated into a single component or can be divided into a number ofdifferent components. Components 1920-1930 can be implemented on anynumber of machines and can be integrated, as desired, with any number ofother functionalities or services. By way of example only, any number ofcomponents of a data-exchange system 1902 may operate in a cloud-basedservice.

As previously described, the data-exchange system 1902 can execute in acontainer-managed environment (e.g., a Kubernetes environment). In thisregard, various components 1920-1930 can execute in connection withaspects of a container-managed environment such as jobs and/or pods.

The data exchange service 1920 (e.g., a collect service) is generallyconfigured to initiate and manage deployment of the execution manager1922. To initiate deployment of the execution manager 1922, the dataexchange service 1920 may initiate or trigger a managing job based on adata exchange request. A data exchange request generally refers to arequest to exchange data (e.g., collect or export data) or an aspectassociated therewith. One example of a data exchange request is a datacollection request, which generally refers to a request to collect dataor an aspect associated therewith (e.g., initiate a job). The dataexchange service 1920 may obtain a data exchange request (e.g., datacollection request) in any number of ways. As one example, a dataexchange request may be received from a user device, such as clientdevice 1804 of FIG. 1. In particular, a user may select or input datathat is used to generate a data exchange request. In some embodiments, arestful API may be used to input such data for a data exchange request.Upon obtaining data (e.g., via a user selection or input), the dataexchange request can be generated and communicated to the collectservice 1920.

A data collection request may include any number of data used to executedata collection. In some implementations, a data collection request mayinclude one or more data collection parameters. Data collectionparameters may be any type of attribute related to data collection. Byway of example, and not limitation, data collection parameters mayinclude an indication of data to collect (e.g., a data or data setidentifier indicating data a connector is to collect from a datasource). Data to collect may be indicated in any number of ways. In somecases, data to collect may be specified via a bucket identifieridentifying a bucket from which to read data. In other cases, data tocollect may be specified via a data path indicating a location at whichdata is stored. As another example, data collection parameters mayinclude an indication of a connector (connector identifier) to utilizefor collecting data. As previously described, a connector may correspondwith a specific data source. As such, the connector corresponding with aspecific data source from which data is desired to be collect may beidentified via a data collection parameter. Another data collectionparameter may include, an indication of a manner in which to scale datacollection (e.g., maximum number of pods, maximum amount of computeresource per pod (e.g., 10 CPUs) or other aspects associating withscalability. Another data collection parameter may include an indicationof a data collection schedule, for example, a schedule related to thediscover process and/or collect process. Another data collectionparameter may include credentials, for example, to access data via adata source.

In operation, upon obtaining a data exchange request (e.g., a datacollection request), the data exchange service 1920 may initiate amanaging job 1940 (also may be referred to as a data collection job). Inthis regard, the data exchange service 1920 may use data exchangeparameters included in a data exchange request (e.g., received from aclient device) to initiate and/or create managing job 1940. The managingjob 1940 may be a job on which an execution manager 1922 can bedeployed.

In some cases, the data exchange service 1920 can directly create themanaging job 1940. In other cases, the data exchange service 1920 caninitiate creation of the managing job 1940 via a job initiator 1942(e.g., a cron job, such as a Kubernetes CronJob in instances in whichthe Kubernetes orchestration platform is used). In such a case, the dataexchange service 1920 can create a job initiator 1942, such as aCronJob, in response to the data exchange request. For instance, thedata exchange service 1920 may use input, such as data exchangeparameters, from the user to create a job initiator 1942 (e.g., via anAPI call).

A job initiator 1942, such as a CronJob, may refer to a time-based jobscheduler. When the job initiator 1942 is triggered (e.g., via the dataexchange service 1920), the job initiator 1942 can create the managingjob 1940A. For instance, the job initiator 1942 can create a cronexpression that represents a set of times, or schedules a time, tocreate managing job 1940.

Upon creating the managing job 1940 (e.g., via the data exchange service1920 and/or the job initiator 1942), an execution manager 1922 can bedeployed via the job manager 1940. The execution manager 1922 maydeployed in any of a number of ways, such as via the data exchangeservice, the job initiator 1942, or another component. As one example, acall between the data exchange service 1920 and managing job 1940 may beused to deploy the execution manager 1922. As another example, the jobinitiator 1942 may deploy the execution manager 1922. As yet anotherexample, the job initiator 1942 may deploy another component (e.g., ajob trigger), which may then deploy the execution manager 1922.

In addition to initiating deployment of the execution manager 1922, thedata exchange service 1920 may manage deployment of the executionmanager 1922. Deployment of the execution manager 1922 may be managed bymonitoring data exchange configurations (e.g., data collectionconfiguration) and data exchange execution (e.g., data collectionexecution). Data exchange configurations generally refer toconfigurations associated with the execution manager 1922, that is,configurations applied to and/or used by the execution manager 1922.Data exchange configurations can include information related to, forexample, job identifier (e.g., managing job identifier), job schedule(e.g., schedule for executing a job), scaling (e.g., number ofcoordinators and/or connectors), connector specific configurations(e.g., configuration required by a connector to exchange (e.g., collect)data, such as credentials including username, password, API key, or thelike the connector uses to connect the data source), or the like. As canbe appreciated, such data exchange configurations, or portions thereof,can be, or identified by the data exchange service 1920 from, dataexchange parameters included in a data exchange request provided via aclient device. Data exchange configurations can be stored in a datastore, such as data store 1944. In some implementations, data store 1944may be a data store that is at least partially used to store sensitivedata such as passwords, tokens, and certificates, such as DockerSecrets, SecretHub, Vault, or another secret manager.

In monitoring data exchange execution, the data exchange service maymonitor execution data, such as collection execution data. Executiondata generally refers to any data related to data exchange execution.Collection execution data may refer to any data related to executingdata collection. As such, the data exchange service 1920 may obtainexecution data via the execution manager 1922. For example, as executionof data collection is performed, the execution manager 1922 may providethe data exchange service 1920 with collection execution data (e.g.,indications of executing jobs, state of execution, etc.). The dataexchange service 1920 may then create or update execution records in adata store, such as data store 1946. Execution records may includevarious types of information related to execution, such as indicationsof executing jobs, the state of the execution, or the like. One exampleof a data store 1946 may be a database that supports key-value and/ordocument data structures, such as DynamoDB. MongoDB, and ApacheCassandra. Although data store 1944 and 1946 are illustrated as twoseparate data stores, as can be appreciated, the data stores may becombined into a single data store or may be separated into any number ofdata stores.

The execution manager 1922 is generally configured to manage executionof data exchange, such as data collection. As previously described, theexecution manager 1922 may be deployed on the managing job 1940. Theexecution manager 1922 generally manages discover coordinator 1924,connector 1926, data exchange coordinator 1928, and connector 1930. Asthe data-exchange system can operate via a discover process and a dataexchange phase, the execution manager 1922 may mange both processes toperform data collection.

To manage data exchange, the execution manager 1922 can manage variousjobs (e.g., create and/or terminate) to execute data exchange (e.g.,data collection). In this regard, the execution manager 1922 caninitiate jobs 1950 and 1952 of the discover process as well as jobs 1954and 1956 of the data exchange process. Each of the jobs 1950-1956 cancreate and manage a corresponding pod(s) for executing a process ortask. In this regard, job 1950 may create pod 1960, job 1952 may createpod 1962, job 1954 may create pod 1964, and job 1956 may create pod 1966to execute various processes or tasks as well as track the completion ofthe particular process or task. In embodiments, the execution manager1922 may initiate jobs 1950-1956 in accordance with, or based on,various data exchange parameters (e.g., data collection parameters). Forexample, jobs 1950-1956 may be created in accordance with a schedule,e.g., as specified by a user. Such data exchange parameters may beobtained by the execution manager 1922 via the data exchange service1920, the job initiator 1942, the data store 1944, or other component.For example, in accordance with executing a managing job, or datacollection job, the execution manager 1922 may retrieve data collectionparameters or configurations (e.g., by calling a private API on collectservice 1920).

As described, a pod (e.g., pods 1960-1966) is created and managed by thecorresponding job. A pod can represent an instance of a task, process,or application. In this regard, a pod can perform (e.g., via acontainer(s)), the operation or execution of a task, process,application, or workload. Pods can be created and/or terminated asneeded to conform to desired parameters (e.g., specified by a user).Generally, a pod is or includes a logical collection of one or morecontainers that can operate together.

In accordance with initiating or creating the jobs and correspondingpods, the discover coordinator 1924, connector 1926, data exchangecoordinator 1928, and connector 1930 can be deployed in association withthe corresponding job/pod. In embodiments, the execution manager 1922initiates and/or facilitates deployment of the discover coordinator,data exchange coordinator, and collectors on the correspondingjobs/pods. In some cases, the execution manager 1922 may identify aparticular connector to deploy to communicate with the appropriate datasource. For example, the data collection parameters described above mayidentify a particular connector. In some implementations, executionmanager 1922 may determine a particular connector to deploy from datastored in data store 1944. In this regard, the execution manager 1922may detect the data source containing the data to collect and identifyand/or deploy the connector used to collect data from that particulardata source. Although generally described as the execution manager 1922that initiates deployment of the coordinators and collectors, as can beappreciated, other components may initiate such deployments.

The execution manager 1922 may provide data exchange parameters orconfigurations (or portions thereof) to the discover coordinator 1924,the data exchange coordinator 1928, connector 1926 and/or connector1930. In some cases, the execution manager 1922 may provide parametersand/or configurations to the discover coordinator 1924 and the dataexchange coordinator 1928, which may then provide such data to thecorresponding connector 1926 and connector 1930. Such data exchangeparameters or configurations can then be used by the coordinator(s)and/or connector(s) as appropriate to execute data exchange, such asdata collection.

Upon the discover coordinator 1924, the connector 1926, the dataexchange coordinator 1928, and the connector 1930 being deployed, theexecution manager 1922 may manage various aspects related to executionof data exchange (e.g., data collection) via such components. As oneexample, the execution manager 1922 may monitor resources utilized andidentify instances in which to scale or initiate additional components,such as additional data exchange coordinators 1928, connectors 1930, orthe like. The execution manager 1922 may monitor resources in any numberof ways. For instance, in some cases, a connector may provide resourcedata (e.g., CPU usage, memory, etc.) to a coordinator (e.g., dataexchange coordinator, such as a collect coordinator), which maycommunicate the resource data to the execution manager 1922.

In embodiments, the execution manager 1922 may manage scaling ofcomponents, such as data exchange coordinator 1928 and connector 1930,in accordance with, or based on, various data exchange parameters and/orconfigurations. For example, utilized resources may be compared withscaling data collection parameters to determine when to scale acomponent. Data exchange parameters and/or configurations, such asparameters related to scaling, may be obtained by the execution manager1922 via the data exchange service 1920, the job initiator 1942, thedata store 1944, or other component.

Upon determining or identifying to scale a component, such as dataexchange coordinator or connector, the execution manager 1922 mayinitiate such component scaling. For instance, in some cases, theexecution manager 1922 may trigger the corresponding job to initiate orcreate another pod. Upon creation of the pod, the appropriate componentcan be deployed via the pod. By way of example only, assume anadditional connector is desired to collect data via the collectionprocess. In such a case, upon making such a determination, the executionmanager 1922 may trigger the job 1956 (e.g., directly or via the dataexchange coordinator) to create a new pod and, thereafter, may deploy anew connector on the new pod.

In accordance with data exchange execution (e.g., via the discovercoordinator 1924, the connector 1926, the data exchange coordinator1928, and/or the connector 1930), information may be provided back tothe execution manager 1922 for recording (e.g., via a data store) and/orfurther managing the data exchange execution. For instance, dataexecution (e.g., collection) execution status, resource utilization, andthe like may be communicated to the execution manager 1922 (e.g., viathe discover coordinator or the data exchange coordinator). Theexecution manager 1922 can then utilize the information to furthermanage data exchange execution. Additionally or alternatively, theexecution manager 1922 may facilitate storage of such data. Forinstance, execution manager 1922 may provide data collection executionstatus to the data exchange service 1920 for storage in a data store.

As described, in accordance with the execution manager 1922 initiatingexecution of data exchange (e.g., collection), the discover coordinator1924, connector 1926, data exchange coordinator 1928, and connector 1930can operate to perform data exchange (e.g., data collection). Inimplementation, the discover coordinator 1924 and the connector 1926 mayexecute as a discover process, and the data exchange coordinator 1928and the connector 1930 may execute as a data exchange process (e.g., acollect process) such that the functionality may be performed inparallel. In the discover process, the discover coordinator 1924 andconnector 1926 can operate to discover or identify data to exchange(e.g., collect). In the data exchange process, the data exchangecoordinator 1928 and the connector 1930 can operate to exchange data(e.g., collect data).

Turning initially to the discover process, the discover coordinator 1924is generally configured to manage data discovery. To do so, the discovercoordinator 1924 may communicate with the execution manager 1922 and theconnector 1926. In particular, the discover coordinator 1924 mayinitiate data discovery via the connector 1926. To this end, thediscover coordinator 1924 may trigger a function (e.g., RPC function) ofthe connector to initiate data discovery. In embodiments in whichmultiple connectors are initialized, the discover coordinator 1924 maydetermine or select which connector to communicate with. Alternatively,the discover coordinator 1924 may communicate with each initializedconnector. To initiate data discovery, the discover coordinator 1924 mayprovide an indication of the desired data such that the connector 1926(and/or additional connectors) can discover the data. In some cases, thediscover coordinator 1924 may provide a data source identifier, a bucketidentifier, or the like.

The connector 1926 generally discovers or identifies tasks, e.g.,collect tasks, associated with the data to exchange (e.g., collect),which will be used in the data exchange phase by the data exchangecoordinator 1928 and the connector 1930. In this regard, the connector1926, upon initiation (e.g., via a call from the discover coordinator1924), can communicate with the corresponding data source to identifythe particular data to collect from or export to the data source. Inembodiments, the connector 1926 does not review data content (e.g.,content of a file) but recognizes the file and identifies a taskassociated therewith. By way of example only, assume a bucket identifieris provided to the connector 1926. In such a case, the connector 1926can communicate with the appropriate data source (e.g., via an API) toidentify the files in the bucket. Upon identifying the files, theconnector 1926 can generate a list of tasks (e.g., collect tasks)corresponding to the files. For instance, in some cases, the connector1926 may generate a collect task for each file. In other cases, theconnector 1926 may generate a collect task for a set of files (e.g.,10,000 files, or a subfolder of files in a conventional file system witha directory tree). As can be appreciated, tasks are not limited to filesand may be generated in association with other sets of data (e.g.,buckets of data). A task or exchange task may indicate any task that canbe performed by the connector 1930 in the data exchange phase. In thisregard, a collect task may indicate data collection to be performed viathe collect process. In embodiments, the task may include an indicationto read a particular set of data, such as the set of files, and toingest events within the data (e.g., the set of files). The connector1926 may then provide the set of tasks to the discover coordinator 1924.As previously described, in addition to the connector 1926 providingtasks to the discover coordinator 1924, the connector may provide otherdata, such as execution status, etc.

In response to the discover coordinator 1924 receiving data from theconnector, the discover coordinator 1924 may communicate the data to thedata exchange phase, the execution manager 1922, or the like. In thisway, the set of tasks may be provided to the data exchange process. Inembodiments, the tasks may be provided to the data exchange process viaa task queue, such as task queue 1970. In embodiments, task queue 1970may be any sort of message queue or commit log service that allows thetasks to be passed from one component to another, in this case from thediscover coordinator 1924 to the data exchange coordinator 1928. Assuch, upon receiving a task, the discover coordinator 1924 can providethe task or set of tasks to the task queue 1970. As the discovercoordinator 1924 obtains execution data (e.g., collection executiondata) or execution status from the connector, the discover coordinatormay provide such data to the execution manager 1922. Further, in someembodiments, the discover coordinator 1924 may terminate execution ofthe connector 1926, for example, upon the connector 1926 completingidentification of the collect tasks.

Advantageously, as previously described, abstracting the connector fromother data-exchange functionality (e.g., data-collecting functionality)enables efficient and secure data exchange. For example, thisabstraction or separation of connector functionality from otherdata-exchange functionality prevents access to restricted resources inconnection with a data-exchange system that may otherwise be providedvia permissions or authorizations provided to perform connectorfunctionality. Such access prevention is particularly valuable when theconnector functionality is generated or created by a third-party to thedata exchange system (e.g., third-party developers creating code toenable access to data of a third-party data source). Further, as thediscover coordinator 1924 and the connector 1926 execute via differentjobs/pods, the components can use different resources thereby improvingefficiency of the connector.

Turning to the data exchange process, the data exchange process (e.g.,collect process) is generally configured to exchange data throughexecution of the exchange tasks previously described above. Inparticular, the data exchange coordinator 1928 (e.g., collectcoordinator) generally manages data exchange (e.g., data collection),while the connector 1930 generally performs the data exchange (e.g.,data collection). To manage data exchange, the data exchange coordinator1928 may communicate with the execution manager 1922 and the connector1930. In particular, the data exchange coordinator 1928 may initiatedata exchange via the connector 1926. To this end, the data exchangecoordinator 1928 may trigger a function (e.g., RPC function) of theconnector to initiate data exchange (e.g., data collection). Inembodiments in which multiple connectors are initialized, the dataexchange coordinator 1928 may determine or select which connector tocommunicate with. Alternatively, the data exchange coordinator 1928 maycommunicate with each initialized connector.

To initiate data exchange, the data exchange coordinator 1928 mayprovide a task or set of tasks to the connector 1930 such that theconnector 1930 (and/or additional connectors) can exchange (e.g.,collect) the data in accordance with the task. As described above, thetask may be generated via connector 1926 in the discover process. Thecollect coordinator 1928 may obtain the task (e.g., via a push or pull)from a task queue 1970. As such, the data exchange coordinator 1928 mayobtain a task or set of tasks from a task queue and assign the task orset of tasks to a connector, such as connector 1930.

In assigning a task or set of tasks, the data exchange coordinator 1928may account for resources utilized by the connector, among other things.In this way, the data exchange coordinator 1928 may manage data exchange(e.g., data collection) in a more efficient manner. For example, assumea first connector is utilizing more resources than a second connector.In such a case, a new set of tasks may be analyzed and assigned to thesecond connector. In other embodiments, another component may accountfor resources utilized by the connector. For example, a component (e.g.,component corresponding or executing within the same pod as theconnector) may monitor resource utilization of the connector. Whenresource utilization exceeds a threshold, the component may hide orotherwise prevent the data exchange coordinator 1928 from assigning atask(s) to the particular connector and/or prevent a particularconnector from accepting additional tasks.

Additionally or alternatively, resource utilization may be used tomanage resource scaling associated with the connector. That is, based onresource utilization, resources dedicated to a pod and/or connector maybe increased or decreased. For example, a component (e.g., a dataexchange coordinator 1928 or component corresponding or executing withinthe same pod as the connector) may monitor resource utilization of theconnector. When resource utilization exceeds a threshold, the amount ofresources for use by the connector and/or corresponding pod may beincreased (e.g., via the data exchange coordinator 1928).

Resource utilization may also be used to manage scaling of connectorssuch that additional connectors are instantiated to perform dataexchange. In this regard, data exchange coordinator 1928 (e.g., inconjunction with execution manager 1922) may instantiate one or more ofanother job 1956, another pod 1966, and/or another connector 1930, inorder to scalably complete the tasks retrieved by the collectcoordinator 1928 from the task queue 1970. The decision of whether toexecute another job 1956, to assign another pod 1966 to job 1956, or toassign another connector 1930 (optionally inside of another container)to pod 1966, is dependent upon the system design choice, and, in someimplementations, may be at least partially specified by the executionmanager 1922 according to the data exchange parameters describedpreviously.

Upon connector 1930 obtaining a task or set of tasks, connector 1930generally exchanges (e.g., collects) the appropriate data in accordancewith the task. In this regard, the connector 1930, upon being triggered(e.g., via a call from the data exchange coordinator 1928), cancommunicate with the corresponding data source to collect the particulardata from the data source. By way of example only, assume a first taskis provided to the connector 1930. In such a case, the connector 1930can communicate with the appropriate data source (e.g., via an API) tocollect the appropriate data. For instance, the connector 1930 may readan identified set of data (e.g., a set of files) and obtain or ingestevents within the data (e.g., the set of files). The connector 1930 maythen provide the collected data to the data exchange coordinator 1928.As previously described, in addition to the connector 1930 providingcollected data to the data exchange coordinator 1928, the connector 1930may provide other data, such as execution status, etc.

In response to the data exchange coordinator 1928 receiving data fromthe connector 1930, the data exchange coordinator 1930 may communicatethe data to a data-processing system (e.g., an index), a pipelinestream, or other component, interface, or bus. In this way, collecteddata (e.g., a set of events) may be provided to a data-processing systemfor indexing, storage, and/or analysis. As the data exchange coordinator1928 obtains execution data or execution status from the connector, thedata exchange coordinator may provide such data to the execution manager1922. Further, in some embodiments, the data exchange coordinator 1928may terminate execution of the connector 1930, for example, upon theconnector 1930 completing identification of the collect tasks.

Advantageously, as previously described, abstracting the connector 1930from other data-exchanging functionality enables efficient and securedata exchange, such as data collection. For example, this abstraction orseparation of connector functionality from other data-collectingfunctionality prevents access to restricted resources in connection witha data-exchange system that may otherwise be provided via permissions orauthorizations provided to perform connector functionality. Such accessprevention is particularly valuable when the connector functionality isgenerated or created by a third-party to the data exchange system (e.g.,third-party developers creating code to enable access to data of athird-party data source). Further, as the data exchange coordinator 1928and the connector 1930 execute via different jobs/pods, the componentscan use different resources thereby improving efficiency of theconnector.

As can be appreciated, any number of data exchange coordinators and/orconnectors can operate in the data exchange process (e.g., viacorresponding pods). For example, in some implementations, a single dataexchange coordinator may execute in connection with one or moreconnectors. In other implementations, a data exchange coordinator may beinitiated to execute in connection with a particular connector (e.g., a1-to-1 relationship such that a different data exchange coordinatorexecutes for each connector).

In accordance with embodiments described herein, components of the dataexchange process may be scaled to increase efficiency of datacollection. For example, in some embodiments, connectors and/orcorresponding pods may be scaled to increase efficiency in performingdata collection or exportation. For instance, a component (e.g., a dataexchange coordinator, an execution manager, a component corresponding orexecuting within the same pod as the connector, another Kubernetescomponent, or the like), may monitor resource utilization of theconnector. Any resource utilization may be monitored, such as memory,CPU, or other metric. When resource utilization exceeds a threshold,another connector may be initiated via another pod. A similar processmay be used to scale down connectors based on resource metrics. Inimplementation, any number of components may initiate the new connectorand/or corresponding job. For instance, a data exchange coordinator, anexecution manager, or combination of such components may initiate a newpod and/or deploy a new connector in association with the new pod. Asanother example, connector scaling may be provided via anothercomponent, such as a component(s) that performs horizontal podauto-scaling (e.g., via Kubernetes).

Although resource and/or component scaling is generally discussed inrelation to connectors of the data exchange process, as can beappreciated, such scaling may be applicable to other aspects of thedata-exchange system. For example, in some cases, scaling may occur inconnection with the data exchange coordinator of the data exchangeprocess. As another example, scaling may occur in connection with thediscover coordinator 1924 and/or the connector 1926 of the discoverprocess.

In some cases, in accordance with performing data exchange (e.g., datacollection), information or an alert may be provided via a graphicaluser interface to alert or notify a user of the status of data exchange(e.g., data collection initiation, data collection progress, datacollection termination, data collection error, or the like).

3.2 Scalable and Secure Data-Exchange Methods

FIGS. 20-22 illustrates method of facilitating efficient data exchangein a secure manner, in accordance with embodiments of the presentinvention. As can be appreciated, additional or alternative steps orblocks may also be included in different embodiments. Methods 2000,2100, and 2200 may be performed, for example, at a data-exchange system,such as data-exchange system 1902 of FIG. 19. FIGS. 20-22 are generallydescribed in relation to efficient data collection in a secure manner.One skilled in the art can appreciate that similar methods can beemployed to facilitate other efficient data exchange in a secure manner,such as exporting data in a secure manner.

Turning initially to FIG. 20, FIG. 20 provides a method for enablingefficient data collection in a secure manner. At block 2002, at acollect service, a data collection request including a set of datacollect parameters is obtained. At block 2004, a data collectionparameter of the set of data collect parameters is used to initiatedeployment of an execution manager. An execution manager generallymanages execution of data collection. In embodiments, an executionmanager can execute on a managing job of a container-managed platform.At block 2006, deployment of a collect coordinator is initiated, via anexecution manager, on a first pod associated with a first job. Thecollect coordinator is generally configured to manage the firstconnector. At block 2008, deployment of a first connector on a secondpod associated with a second job is initiated via the execution manager.The first connector is generally configured to collect data from a datasource. In embodiments, the first job and second job are separate fromone another within a container-managed platform. At block 2010, a taskto collect a set of data from a data source is provided, via the collectcoordinator deployed on the first pod of the first job, to the firstconnector deployed on the second pod of the second job. At block 2012,the set of data from the data source is obtained via the firstconnector. The first connector then provides the set of data to thecollect coordinator, as illustrated at block 2014, for providing the setof data to a remote source, such as a communication bus or adata-processing system.

With reference now to FIG. 21, FIG. 21 provides another method forenabling efficient data collection in a secure manner. At block 2102, adata collection request is obtained at a collect service. Such a datacollection request can include one or more data collection parameters.At block 2104, the collect service initiates a managing job (e.g., usinga data collection parameter(s)). In some cases, the collect service candirectly create the managing job. In other cases, the collect servicecan initiate creation of the managing job via a job initiator (e.g., acron job, such as, CronJob). Upon creating the managing job, at block2106, an execution manager is deployed via the job manager. Asdescribed, an execution manager generally manages execution of datacollection. At block 2108, the execution manager initiates a first joband second job for a discover process. The discover process is generallyconfigured to discover which data to collect. At block 2110, theexecution manager initiates a first job and second job for a collectionprocess. The collection process is generally configured to collect theidentified data. At block 2112, the execution manager obtains data(e.g., resource utilization, execution status, etc.) via the discoverprocess and/or collection process. In this regard, upon initiating thejobs for the discover process and the collection process, such processescan provide information (e.g., execution status) back to the executionmanager to facilitate management of data collection execution. At block2114, the execution manager uses the obtained data to manage datacollection execution.

Turning to FIG. 22, FIG. 22 provides another method for enablingefficient data collection in a secure manner. At block 2202, a discovercoordinator provides an indication of desired data to a connector fordiscovering data. For example, a discover coordinator may provide a datasource identifier, a bucket identifier, or the like, to a connector. Atblock 2204, the connector discovers or identifies tasks associated withthe data to collect. In embodiments, a task may include an indication toread a particular set of data, such as a set of files, and to ingestevents within the data. At block 2206, the connector provides the tasksto the discover coordinator. At block 2208, the discover coordinatorcommunicates tasks to a queue. At block 2210, a collect coordinatorobtains a task via the queue. The collect coordinator provides a task toa connector to collect data in accordance with the task, as indicated inblock 2212. At block 2214, the connector collects appropriate data, viaa data source, in accordance with the task. The collected data isprovided to the collect coordinator for providing to a remote source, asillustrated at 2216.

4.0 Overview of Facilitating Efficient Message Queuing

Large-scale data collection (LSDC) services (e.g., a LSDC servicedescribed herein), or other data-exchange service, can operate in acloud environment and/or an on-premises environment. For example, someusers may prefer a cloud service environment in which the user is notdirectly responsible for providing and managing the computing devicesupon which various components of a large-scale data collection servicemay operate, while other users may prefer an on-premises solution suchthat the large-scale data collection service is operated on the user'sown computing infrastructure (e.g., to provide a greater level ofcontrol over the configuration of certain aspects of the service).Irrespective of whether a data-exchange service, such as a large-scaledata collection service, is deployed in a cloud environment and/oron-premises environment, it may be desirable to have the data-exchangeservices operate in a similar manner to one another, for example, from auser's perspective. To this end, it may be desirable that an on-premisesLSDC service operates in a way that is not substantially different orlimited in functionality compared to a LSDC service operating in a cloudenvironment, and vice versa.

In a data-exchange environment, an external or third-party service(s)may be used to implement data-exchange (e.g., large-scale datacollection) functionality. In this regard, a data-exchange serviceoperating in a cloud environment may use cloud-specific externalservices to perform various functionalities. By way of example only, incases that AWS® is utilized to implement a LSDC cloud environment, AWS®services, such as Simple Storage Service (S3), Simple Queue Service(SQS), Kinesis, DynamoDB, and Secrets Manager, may be used. Suchservices may be used to implement various functionalities, includingcheckpoint persistence, ingest task distribution, forwarding ingestedevents, job scheduling persistence and execution status, and storage ofsensitive job parameters, respectively. In an on-premises environment,however, cloud-specific services (e.g., AWS®-specific service) may beunavailable. As such, for a LSDC service deployed in an on-premisesenvironment to operate in a similar manner as a LSDC service deployed ina cloud environment, an on-premises replacement service(s) can be usedto effectuate a similar operation as performed via a cloud-specificservice in a cloud LSDC environment. One example of an on-premisesreplacement service that may be used in an LSDC on-premises environmentis a message or task queuing service. In particular, a message or taskqueue service that operates in a similar manner (e.g., from a user'sperspective) as that utilized in a LSDC cloud environment (e.g., SQS)may be desired.

One type of on-premises replacement service that may be utilized as amessage or task queueing service in an on-premises LSDC environment maybe a processing service that performs data processing in a streamingmanner. In this way, streaming data processing may be used to performservices for queuing messages or tasks. One example of a streamingplatform that may be used to implement streaming data processing isApache Kafka®. Generally, streaming data processing involves batchprocessing. In this regard, in a streaming system, messages areprocessed in a batch such that additional messages cannot be processeduntil the initial, or previous, batch of messages are acknowledged orcommitted. To this end, in stream processing, a sequence of messages aregenerally acknowledged up to a particular offset (e.g., in a topic) andmessages cannot be acknowledged individually. In utilizing streamingdata processing to process messages, such batch processing of sequencesof messages instead of individual messages is oftentimes inefficient,particularly when messages correspond with different processing times.For example, assume two messages take one minute to process and anothermessage takes twenty minutes to process. Further assume that a streamingdata processing system reads three messages as a batch before moving tothe next batch of three messages. In such a case, it can be inefficientto wait for all three messages, including the message that takes twentyminutes to process, to be acknowledged or committed before reading thenext batch of three messages. In addition, message processor failure andrecovery may result in redundant message processing. For example, assumea second message and a third message are processed successfully, while afirst message continues to be processed. Further assume a restart isnecessitated resulting in a message processor resuming from the lastcommitted offset. In such a case, the message processor will read thefirst, second, and third messages thereby duplicating reading of thefirst and second messages.

As such, embodiments described herein provide a message queuing servicethat acknowledges processing of individual messages. Accordingly,instead of acknowledging sequences of messages, embodiments describedherein acknowledge, or commit offsets, for individual messages. Byacknowledging individual messages, the message queueing service can moreefficiently proceed with reading a next message. In accordance with someembodiments described herein, such a message queuing service enablingacknowledgment of individual messages can be implemented in connectionwith, or on top of, a stream processing platform, such as Apache Kafka®.

In some cases, upon consuming a message from the stream, initiatingmessage processing, and reading a subsequent message from the stream,processing of the initial message may not be completed. For instance,while a message is being processed, a processing component may fail tofunction thereby preventing completion of the message processing. Insuch cases, the message is essentially gone as the message queueingservice has committed a message offset and proceeded to read a nextmessage, but the message was not fully processed. Advantageously, themessage queueing service described herein performs message redeliverysuch that messages can be redelivered to a queue for processing ininstances that the message was not fully processed.

To facilitate message redelivery, a sequence of markers associated witha task or message can be used to identify instances in which toredeliver the message for processing. The sequence of markers indicateprocessing states, such as when a process associated with a message isstarted (start state), when a process associated with a messagecontinues or is still alive (in-process or keep-alive state), and when aprocess has been completed (end state). In monitoring the markersassociated with a message or task, a determination can be made as towhen to redeliver a message for processing. In particular, when aredelivery deadline has expired without processing being completed for amessage, the message can be redelivered for processing. In some cases,each message may include a unique and/or configurable redeliverydeadline, which can be indicated via start and keep-alive markers. Asthe message processing times vary, monitoring the processing state(e.g., keep-alive state) can facilitate refreshing or updating aredelivery deadline. In this regard, a message associated with a longerprocessing time and thereby having a lengthy keep-alive state will notbe redelivered until an appropriately refreshed redelivery deadline hasexpired. In other words, the redelivery deadline can be extended toaccommodate for the longer processing time, thereby preventing anunnecessary redelivery of the message.

In implementation, to facilitate message redelivery, a queue topic maybe used to hold messages or tasks to process and a markers topic may beused to hold markers indicating processing states associated withmessages or tasks. As messages from the queue topic are consumed, aprocessing state associated with the messages can be monitored. Markerscan be generated and/or written to the markers topic to indicate suchprocessing state (e.g., start state, keep-alive state, and/or endstate). The markers in the markers topic can then be monitored by aredelivery monitor to identify when to redeliver messages forreprocessing. Generally, upon expiration of a redelivery deadline (orrefreshed redelivery deadline) and when an end state for a task has notbeen achieved, message redelivery may occur. In addition to theefficiencies provided by maintaining and utilizing keep-alive states torefresh redelivery deadlines, the redelivery monitor described hereincan utilize queue multiplexing, message preservation, time to live(TTL), and/or back-pressure to increase efficiency of various aspects ofmessage queueing, including message redelivery.

Although embodiments described herein are generally described inrelation to a data-exchange service, such as a LSDC service, embodimentsare not intended to be limited herein and can be used in variousimplementations in which an efficient queueing service is desired,particularly in connection with a streaming data processing service(e.g., Apache Kafka®). In this regard, the message queueing servicedescribed herein is a general purpose queuing service that can be usedin various implementations, one of which is a data-exchange service(e.g., LSDC service).

4.1 Overview of an Efficient Message Queuing Service

As described, embodiments herein are directed to providing an efficientmessage queuing service. In particular, efficient message queuingservices are provided to facilitate acknowledging individual messages inconnection with, or on top of, a streaming data processing system (e.g.,Apache Kafka®). By acknowledging individual messages, a set of messagescan be processed more efficiently and result in a less expensiverecovery from processing failure as redundant message processing isreduced or avoided. In some cases, however, some messages may need to beredelivered for processing as an individual message may be consumed froma data stream (e.g., queue stream) but the processing of the message maynot have been completed. As such, a redelivery monitor may be used toefficiently redeliver messages when needed. To increase efficiency ofmessage queuing in accordance with embodiments discussed herein, variousaspects, as more fully described below, can be used including queuemultiplexing, redelivery deadline refreshing, message preserving,time-to-live for message redelivery, back-pressure, and/or the like.

Turning to FIG. 23, FIG. 23 illustrates a message queuing service 2300for efficiently and effectively performing message queueing services. Amessage can include any type of information. Generally, a message may bea byte array that can store any object in any format. In someembodiments described herein, a message is, or includes, a task, such asa task or exchange task described in association with a data-exchangesystem of FIG. 19, a marker indicating a processing state, and/or thelike. To distinguish between messages stored in a queue topic andmessages stored in a markers topic, messages for processing from a queuetopic are generally referred to herein as messages or tasks and messagescorresponding with a markers topic are generally referred to herein asmarkers.

The message queueing service 2300 includes a set of logical queues 2310,a queue topic 2320, a markers topic 2330, and a redelivery monitor 2340.Message queueing service 2300 of FIG. 23 is intended to provide anexample of a general message queueing service that may be used in anynumber of implementations or systems. As one example, message queueingservice 2300 may be implemented in a data-exchange system, such asdata-exchange system 1902 of FIG. 19. In particular, the messagequeueing service 2300 may be implemented as the task queue 1970 of FIG.19 to facilitate providing large-scale data collection services or otherdata-exchange services.

The queue topic 2320 refers to a topic that contains messages to beprocessed, such as tasks. The markers topic 2330 refers to a topic thatcontains markers indicating processing states associated with messages(e.g., tasks). A topic generally refers to a category to which messagesare stored and published. The queue topic 2320 and the markers topic2330 may have a set of partitions 2322 and 2332, respectively. A topiccan be divided into any number of partitions. A partition generallycontains messages or markers, or a queue of messages or markers. Inembodiments, the sequence of messages or markers in a partition isunchangeable, with each message or marker in a partition assigned andidentified by a unique offset. Multiple partitions in a topic can enableconsumers to read from the topic in parallel.

The set of logical queues 2310 refers to one or more logical queues thatmay obtain messages, or tasks, from a producer(s). A producer generallyrefers to a process that publishes data (push messages) to a topic. Forexample, with reference to FIG. 19, a discover coordinator 1924 may be aproducer that provides tasks to the task queue 1970. In someimplementations, each logical queue may correspond with a job, process,subprocess, or request, such as a data collection request described inassociation with the data-exchange service of FIG. 19. In this regard,each task generated in association with a particular data collectionrequest (e.g., via a discover coordinator) can be obtained at aparticular logical queue. For example, a set of tasks generated inassociation with a first data collection request can be provided to, orobtained by, logical queue 2312, and a set of tasks generated inassociation with a second data collection request can be provided to, orobtained by, logical queue 2314. Each of the tasks may include anidentifier to identify or indicate the particular data collectionrequest associated with the task. As described, any number of producers,such as discover coordinators, may be used to produce tasks or messagescommunicated to the message queuing service 2300. For example, in somecases, a particular producer (e.g., discover coordinator) may generateor produce messages (e.g., tasks) associated with a particular datacollection request. In other cases, a particular producer (e.g.,discover coordinator) may generate or produce messages associated with aset of different data collection requests. Although data collectionrequests are generally described in various embodiments, logical queuescan correspond with other requests, processes, subprocesses, jobs, orthe like.

In accordance with embodiments described herein, the logical queues2310, or corresponding messages, are multiplexed to the queue topic2320. In this regard, a set of logical queues is multiplexed to onequeue topic, such as queue topic 2320, instead of each logical queuecorresponding to an individual queue topic. As previously described, anynumber of logical queues may exist and, as such, any number of logicalqueues can be multiplexed to queue topic 2320. Accordingly, all messages(e.g., tasks) for all data collection requests are collected in thequeue topic 2320 via multiple logical queues.

By multiplexing multiple logical queues to a single queue topic,multiple queue topics do not need to be created and/or deleted. Creatingand/or deleting topics can be inefficient and result in processinglatencies and system instability. For example, assume queue topics arecreated to correspond with messages associated with particular datacollection requests. For instance, a first queue topic obtains messagesassociated with a first data collection request, and a second queuetopic obtains messages associated with a second data collection request.In such a case, upon each data collection request creation, a queuetopic would be created. In instances that queue topics are dynamicallycreated and/or deleted, a processing latency can be imposed in order togenerate the queue topic as background processes to create and/orterminate topics competes with data processing traffic.

In embodiments, the messages of the logical queues can be written ordistributed to any partition of the queue topic 2320. In particular,messages can be spread across partitions, irrespective of a particularcorresponding data collection request or logical queue. In other words,a message is not dedicated to a particular partition, for example, basedon its association with a logical queue or data collection request), butinstead, can be provided to a partition at random. In this way, logicalqueues and/or data collection requests do not have a one-to-onecorrespondence with partitions. Although messages can be written to anypartition (e.g., randomly), uniform distribution may be employed tobalance the number of messages associated with each partition. As such,a random, uniform distribution may be applied to distribute messagesacross partitions.

As the messages are spread across partitions 2322 of the queue topic2320, multiple consumers can consume the messages from the variouspartitions. A consumer generally refers to a process that reads messagesfrom a topic, and in particular, topic partitions. For example, withreference to FIG. 19, a data-exchange coordinator 1928 may be, orinclude, a consumer that consumes tasks from the task queue 1970.Consumers can read messages starting from a specific offset. Asdescribed, any number of consumers, such as data-exchange coordinators,may be used to consume tasks or messages in the queue topic 2320. Insome embodiments described herein, a consumer can read messages (e.g.,tasks) from all partitions and filter to process messages associatedwith a data collection request(s) of interest. In some cases, aparticular consumer (e.g., data-exchange coordinator) may consumemessages associated with a particular data collection request. In suchcases, the consumer can read messages across partitions and filter themessages to the data collection request of interest. For example, aparticular consumer recognizes what data collection request it is amember of and, if a message is identified for that data collectionrequest (e.g., via a data collection identifier), the consumer caninitiate processing of the message. Otherwise, messages correspondingwith other data collection requests can be filtered out such that theconsumer does not initiate processing of those messages.

By way of example only, FIG. 24A illustrates an example implementationof multiplexing multiple logical queues, or messages associated withmultiple data collection requests, to a single topic. As described,multiplexing multiple logical queues to a single topic can providevarious efficiencies. As shown in FIG. 24A, a single topic 2402, such asa queue topic, is provided. The topic 2402 includes two partitions, afirst partition 2404 and a second partition 2406. In this example, eachmessage (e.g., task) generated in association data collection request Aand data collection request B is provided to topic 2402. Message_(A1)2410 and message_(A2) 2412 associated with data collection request A mayexist within a first logical queue 2420, and message_(B1) 2414 andmessage_(B2) 2416 associated with data collection request B may existwithin a second logical queue 2422.

As illustrated, message_(A1) 2410 is provided to the first partition2404, message 2412 is provided to the second partition 2406,message_(B1) 2414 is provided to the second partition 2406, andmessage_(B2) 2416 is provided to the first partition 2404. As shown anddescribed herein, the messages can be assigned to any of the partitions(e.g., via random uniform distribution). A consumer, such as coordinator2430, can then consume the messages in both the first partition 2404 andthe second partition 2406. In cases that the coordinator 2430 isconfigured to consume messages associated with a particular datacollection request, the coordinator 2430 can filter the messages toprocess messages associated therewith. For example, assume coordinator2430 is configured to consume messages associated with data collectionrequest A. In such a case, the coordinator 2430 can read each ofmessage_(m) 2410, message_(A2) 2412, message_(B1) 2414, and message_(B2)2416 from the first partition 2404 and the second partition 2406, andfilter the messages such that message_(A1) 2410 and message_(A2) 2412are processed while message_(B1) 2414 and message_(B2) 2416 are filteredor dropped.

In contrast, and with reference to FIG. 24B, FIG. 24B illustrates anexample implementation of providing messages associated with multiplelogical queues and/or data collection requests to multiple topics thatcorrespond with the particular data collection request. Assume a firsttopic 2450 is created in association with a first data collectionrequest A, and a second topic 2452 is created in association with asecond data collection request B. As shown in FIG. 24B, the first topic2450 includes two partitions, a first partition 2454 and a secondpartition 2456. The second topic 2452 also includes two partitions, afirst partition 2458 and a second partition 2460. Message_(A1) 2480,message_(A2) 2482, and message_(A3) 2484 associated with data collectionrequest A may exist within a first logical queue 2462, and message_(B1)2486 and message_(B2) 2488 associated with data collection request B mayexist within a second logical queue 2464.

In this example, each message (e.g., task) generated in association withdata collection request A is provided to the first topic 2450, and eachmessage (e.g., task) generated in association with data collectionrequest B is provided to the second topic 2452. As illustrated,message_(A1) 2480 and message_(A2) 2482 are provided to the firstpartition 2454 of the first topic 2450, and message_(A3) 2484 isprovided to the second partition 2456 of the first topic 2450. ConsumerA 2470 consumes the messages in the first partition 2454 and the secondpartition 2456 of the first topic 2450. Further, message_(B1) 2486 isprovided to the first partition 2458, and message_(B2) 2488 is providedto second partition 2460. Consumer B 2472 consumes the messages in thefirst partition 2458 and the second partition 2460. In this case, themessages associated with a particular data collection request areprovided to a particular topic and consumed by a particular consumer. Assuch, in cases that multiple data collection requests exist, multipletopics are also needed.

Returning to FIG. 23, in accordance with reading a message (e.g., aconsumer reading a task associated with a particular data collectionrequest(s)) from the queue topic 2320, the message can be processed andmonitored such that the state of the message processing can be writtento the markers topic. For example, a consumer, such as a data-exchangecoordinator, can initiate processing of the message, monitor the messageprocessing, and write markers (e.g., as a producer) indicating theprocessing state to the markers topic 2330. As can be appreciated, acoordinator, such as a data-exchange coordinator, may be, or include, aconsumer of messages associated with the queue topic 2320 and a producerof markers associated with the markers topic.

By way of example only, in operation, a coordinator (e.g., data-exchangecoordinator 1928 of FIG. 19) can initially read a message from apartition of the queue topic 2320. The coordinator can then write astart marker to the markers topic 2330 and commit a messaging offset. Anoffset may be a numerical offset, or unique identifier, associated witha message within a partition. The offset can denote the position of aconsumer in association with a partition. For example, a consumerassociated with position or offset 5 has consumed records with offsets 0through 4 and will next receive record with offset 5. A committed offsetindicates that all messages up to the particular offset have been readby the consumer or initially processed.

The coordinator can also initiate processing of the message (or taskexecution), for example, via a connector. In some implementations, thecoordinator may utilize back-pressure to facilitate efficiency of themessage queueing service. Back-pressure refers to a limit on a number ofmessages that are consumed. As such, back-pressure can be incorporatedinto functionality of consumers, such as a coordinator. Utilizing backpressure can limit the number of messages being processed and associatedmarker production.

As the processing occurs, the coordinator can write keep-alive markersto the markers topic 2330. Such keep-alive markers may be written on aperiodic basis during message processing, or in accordance with anoccurrence of an event during processing. For instance, a coordinatormay initiate a connection with a connector and, thereafter, provide atask to the connector for execution. The connector then executes thetask and keeps the connection open between the coordinator and theconnector. In the LSDC environment, the connector can execute the task,collect data, batch data into events, and provide events to acoordinator via an open connection. While the coordinator is receivingevents or data, the coordinator recognizes that the connector is stillexecuting the task. In such a case, a keep-alive message can begenerated (e.g., periodically) and provided to the markers topic whileit is receiving data. In some cases, a background thread can be used tosend keep-alive markers on a periodic basis asynchronous to receivingevents or data. Upon detecting the completion of message processing, thecoordinator can write an end marker to the markers topic 2330 to denotemessage processing is complete.

Various markers indicating processing state can be written to the set ofpartitions 2332 of the markers topic 2330. In some embodiments, markersassociated with a particular data collection request are all written toa particular partition. In this way, each partition corresponds to aparticular data collection request(s) such that markers associated witha particular data collection request(s) are not spread across partitionsof a markers topic. Providing all markers associated with a particulardata collection request to a same partition can facilitate maintainingan order of the markers. To write markers associated with a particulardata collection request to a particular partition, the coordinator mayhash on the corresponding identifier to identify the particularpartition to which a marker is to be written.

Further, the markers can be written in order of occurrence, such that astart marker precedes a keep-alive marker, which precedes an end marker.Writing markers in order can prevent unnecessary redelivery of messages.For instance, if an end marker is read before a start marker, messageprocessing will no longer be tracked as in-process. As such, uponreading a start marker, a subsequent end marker for the message wouldnot be detected, and the message would be redelivered for processing.

In some implementations, similar to the queue topic, a set of logicalqueues, or set of markers associated with multiple data collectionrequests, can be multiplexed to the single markers topic 2330. As such,the markers topic 2330 can contain markers indicating processing statefor all messages being processed. In some cases, a logical queueassociated with a data collection request is multiplexed across a pairof topics (e.g., queue topic and markers topic). In this way, messagesfor a particular data collection request (logical queue) in the queuetopic are pending processing, and markers for the same data collectionrequest (logical queue) in the markers topic track messages that arecurrently being processed.

The number of partitions associated with the queue topic 2320 may bedifferent from the number of partitions in the markers topic 2330. Thenumber of partitions for a topic may depend on, for example, the amountof throughput desired in connection with the topic. For example, morepartitions may be desired in cases that a large number of queues arebeing multiplexed on a topic and concurrent consumption of messages ormarkers is desired. For instance, assume a markers topic has fivepartitions. Further assume a large throughput is desired such that morethan five redelivery monitors are used to consume all the data. In sucha case, more partitions may be desired in connection with the markerstopic.

The redelivery monitor 2340 is generally configured to manage redeliveryof messages or tasks. As described, the redelivery monitor 2340facilitates management of redelivering messages in the event a crashoccurs (e.g., coordinator or connector crashes) while a message or taskis being processed. In this case, a message offset in association withthe queue topic would have already been committed for the message suchthat the message would essentially be lost or dropped in the event of afailure to complete the message processing. Although only a singleredelivery monitor 2340 is depicted, any number of redelivery monitorsmay be used within a message queueing service. For example, a redeliverymonitor may correspond with a particular number of partitions (e.g., 1,2, 3, etc.), such that a redelivery monitor monitors markers for thecorrelated partitions.

Generally, the redelivery monitor 2340 consumes markers from the markerstopic. As discussed, each message or task may correspond with a sequenceof markers in a partition of the markers topic 2330. The redeliverymonitor 2340 can read the start marker associated with a task. The startmarker can indicate the start or beginning of processing in associationwith a message or task. At a high level, when a particular amount oftime or a particular time, referred to herein as a redelivery deadline,has occurred or expired and an end marker has not been read by theredelivery monitor 2340, the redelivery marker 2340 can initiateredelivery of the message to the queue topic for processing (e.g., by acoordinator). Because the end marker was not received prior toexpiration of the redelivery deadline, it can be assumed that an erroroccurred (e.g., a processor crashed) preventing completion of the taskprocessing. A redelivery deadline may refer to an amount or duration oftime, or a particular time, at which a message is to be redelivered whenan end marker is not received prior to the expiration or occurrence ofthe redelivery deadline. As one example, a redelivery deadline may be atimestamp associated with a marker plus a configurable expiry time(e.g., one hour). Such an expiry time or interval can be configurable ona per-message basis.

In embodiments described herein, a keep-alive marker is written to themarkers topic 2330 to indicate the processing of a message or task isunderway or maintained. As previously described, a keep-alive marker canbe generated and written to the markers topic by a coordinator (e.g.,periodically) when the task is still processing (e.g., via a connector).Advantageously, the keep-alive marker can be used to refresh, extend, orupdate the redelivery deadline. To do so, upon a redelivery monitorreading a keep-alive marker, the redelivery deadline can be extended bya refresh time. A refresh time may refer to an amount of time (timeduration) or a particular time to which to extend a redelivery deadline.In this regard, a refresh time may be added a redelivery deadline, orotherwise used, to extend or delay the redelivery of a message. In somecases, a refresh time (e.g., one hour) may be added to an initialredelivery deadline. For example, assume an initial redelivery deadlineis 24 hours (e.g., following a marker, such as a start marker,timestamp). Further assume a keep-alive marker is obtained. In such acase, the redelivery deadline may be extended to 25 hours from the timeof obtaining the start marker. As such, if an end marker is not obtainedwithin 25 hours from obtaining the start marker, the message can beredelivered. In other cases, a refresh time (e.g., one hour) may beadded to a current redelivery deadline. For example, assume an initialredelivery deadline is 24 hours and two hours have passed since theredelivery deadline, leaving 22 hours until the redelivery deadlineexpires. Further assume a keep-alive marker is obtained. In this case,the redelivery deadline may be extended to 23 hours from the currenttime. As such, if an end marker is not obtained within 23 hours from thecurrent time, the message can be redelivered. As yet another example, aredelivery deadline associated with a particular time may be reset toaccount for a refresh time. For instance, assume a redelivery deadlineis set to expire at 1:30 pm. Further assume that a keep-alive marker isobtained. In this example, the redelivery deadline may be reset orextended to 2:30 pm, as a refreshed redelivery deadline, to account fortime during which the message continues to be processed.

As can be appreciated, a refresh time used to generate a refreshedredelivery deadline may be a same amount or varying amount. For example,each keep-alive marker may be associated with a same refresh time. Insuch a case, each instance a keep-alive marker is obtained, theredelivery deadline is extended by the same refresh time (e.g., onehour). In another example, a marker(s) associated with a task may beanalyzed to determine a refresh time to use to extend a redeliverydeadline. For instance, assume a first keep-alive marker is provided twohours after a start marker. In such a case, the refresh time may be twohours. Now assume a second keep-alive marker is provided three hoursafter the first keep-alive marker (or five hours after the startmarker). In this case, an additional refresh time of three hours (or atotal refresh time of five hours) may be used to extend the redeliverytime.

As described, in some cases, messages are redelivered after a givenamount of time passes since a start marker is sent or consumed. In suchcases, timestamps can be used to identify when to redeliver messages. Inone implementation, to facilitate identification of when to performmessage redelivery, the message redelivery process is impervious toclock skew between distributed system components. To do so, theredelivery monitor 2340 can use the timestamp of the last consumedmarker as current when determining whether a redelivery deadline, orrefreshed redelivery deadline, has expired. For example, assume aredelivery monitor reads start marker S1 with a timestamp of 00:00:00and a redelivery deadline of 00:01:00. Further assume the redeliverymonitor reads start marker S2 with a timestamp of 00:00:30 andsubsequently reads start marker S3 with a timestamp of 00:01:00. In thiscase, the redelivery monitor can identify the current time to be thetimestamp of start marker S3, that is 00:01:00, and accordingly performredelivery of the message associated with start marker S1.

In the absence of a new marker, the redelivery monitor 2340 can use alocal monotonic clock to calculate a logical marker timestamp or“current time” that can be used to determine whether a redeliverydeadline has expired. A logical “current time” can be determined using alocal, monotonic clock to calculate the difference between when the lastmarker was received and “now.” The redelivery monitor can add thisduration to the timestamp of the last consumed message and use theresulting time to calculate whether redelivery deadlines for a messagehas expired. In an example of a logical timestamp triggering redelivery,assume a redelivery monitor reads start marker S1 with a timestamp of00:00:00 and redelivery deadline of 00:01:00. The redelivery monitorremembers the value of its local monotonic clock at the time S1 was read(e.g., 1000 milliseconds). In the absence of new markers available forconsumption, the redelivery monitor (e.g., periodically) calculates thedifference between its local monotonic clock and the time when S1 wasread. When the difference between the local monotonic clock and the timewhen marker S1 was read becomes 10 seconds, the message associated withS1 is redelivered.

To refresh, update, or extend the redelivery deadline, the redeliverymonitor may use data structures (e.g., stored in memory). One exampledata structure may include a priority queue of markers based onredelivery deadlines (also referred to as a redelivery priority queue).A redelivery priority queue may contain markers that are ordered byredelivery deadline. Each marker may be associated with a specificmessage and contain the original message content along with theredelivery deadline. The redelivery monitor can read the top orbeginning of the priority queue and determine if a deadline associatedwith the marker at the beginning of the queue expired. A redeliverymonitor may read the beginning of the redelivery priority queue inaccordance with any schedule, such as periodically (e.g., uponexpiration of each second). If the redelivery deadline associated with amarker at the beginning of the queue has been exceeded or expired, thecorresponding message content can be redelivered. When a keep-alivemarker is received, the redelivery monitor can identify a correspondingmarker in the redelivery deadline queue and move the marker in thepriority queue based on a refreshed redelivery deadline therebyextending the redelivery deadline. In this way, if a particular markeris near the beginning of the redelivery priority queue because theredelivery deadline is near expiration, the reception of a keep-alivemarker effectively moves the corresponding marker down in the redeliverypriority queue such that it is further from expiration. In someimplementations, as described above, the keep-alive marker modifies orextends the expiration deadline via a refresh time, which results inmodification of the corresponding marker in the redelivery priorityqueue as the redelivery priority queue can be ordered by expirationdeadlines.

Another example data structure may include a priority queue of markersordered by their corresponding offsets in the markers topic, alsoreferred to herein as an offset priority queue. An offset priority queuecan be used to identify the state of a redelivery priority queue ininstances in which the redelivery priority queue needs rebuilt (e.g.,due to a crash and subsequent restart of a redelivery monitor) toinclude messages that have not yet been redelivered or completed.Tracking the smallest offset can facilitate such rebuilding of aredelivery priority queue.

In operation, when a start marker associated with a task is read, theredelivery monitor 2340 can write the start marker to both theredelivery priority queue and the offset priority queue, ordered byredelivery deadline and offset, respectively. When an end markerassociated with the task is read, the corresponding start marker can beremoved from both queues.

As described, when a redelivery deadline, or refreshed redeliverydeadline has expired and no end marker has been detected, the redeliverymonitor 2340 can determine to initiate redelivery of a message to thequeue topic 2320. Advantageously, in accordance with embodimentsdescribed herein, message or task data can be included in the markers(e.g., via metadata) written to the markers topic and consumed by theredelivery monitor such that the message or task data can be efficientlyprovided to the queue topic. That is, instead of a redelivery monitorquerying or seeking to locate message data in the queue topic, readingthe data, and then redelivering a message with the message data to thequeue topic, the redelivery monitor can redeliver the message data tothe queue topic without having to seek or obtain such needed informationfrom the queue topic. In operation, message data flows from the queuetopic to the redelivery monitor such that an additional seek back to thequeue topic is not required to obtain the message data for redelivery tothe queue topic, thereby improving efficiency of message redelivery.Further, including message data in association with the markers canprevent data loss within the queue topic from being problematic.

As can be appreciated, in embodiments, the message data, or payload, isredelivered to the queue topic 2320 and, thereafter, may be included ina new message in the queue topic. As such, the new message with theredelivered message data may be written to a same or different partitionthan the original message (e.g., as messages may be distributed viarandom uniform distribution) and may have a different offset. Further,metadata of the new message may be different from the original message,for example, in the case of a TTL that is decremented and attached tothe message when it is delivered to the queue topic.

The redelivery monitor 2340 may also use time-to-live (TTL) tofacilitate efficiency of message redelivery. Time-to-live indicates amaximum number of a times a message can be redelivered by the redeliverymonitor. Using a time-to-live to control redelivery reduces theopportunities for repetitive message redelivery. For example, assume aprocessor reads a message and thereafter crashes. Using implementationsdescribed herein, the redelivery monitor 2340 will redeliver themessage. Assume then a processor (same or different processor) reads amessage and crashes, the redelivery monitor 2340 will redeliver themessage a second time. Without using a TTL to limit message redelivery,this process could continue indefinitely.

In one implementation, TTL can be managed via a counter that countsnumber of a times a message is redelivered. By way of example only,assume a TTL is defined as “3,” that is the maximum number of times amessage can be redelivered is “3.” In such a case, a TTL counter canstart at “2.” When a message is to be redelivered, the redeliverymonitor 2340 can recognize that the counter is at “2,” redeliver themessage, and initiate reduction of the TTL counter to “1.” In cases thatthe TTL counter is positive, the redelivery monitor 2340 can redeliver amessage and the counter can be decremented. In cases that the TTLcounter is reduced to zero, the redelivery monitor 2340 will notredeliver the message. As can be appreciated, other implementations maybe used to limit the number of a times a message is redelivered.

As described, the message queueing service 2300 can be employed invarious implementations, including a data-exchange service. Turning nowto FIG. 25, a message queueing service is implemented in connection witha data-exchange service, such as a LSDC service. As such, with referenceto FIG. 25, the messages are referred to as tasks and various componentsare described in relation to the data-exchange service 1902 of FIG. 19.

As illustrated, task queuing service 2500 includes a queue topic 2504, amarkers topic 2508, and redelivery monitors 2512. The queue topic 2504can obtain tasks to be processed from a set of discover coordinators2502. Any number of discover coordinators 2502 may be employed toprovide tasks. In some cases, one coordinator may correspond with oneparticular data collection request. As described, a discover coordinator2502 can generate tasks and/or communicate such tasks to the queue topic2504. The tasks can include a request identifier, a task payload, a TTL,and/or the like. The request identifier can be used for, among otherthings, consuming the tasks (e.g., via a data-exchange coordinator1928). For example, a task may include an identifier of a datacollection request initiating the task or a job that produced the taskto allow for efficient filtering of tasks by the data-exchangecoordinator 2506.

The queue topic 2504 can include any number of partitions. The tasks canbe provided to any of the partitions of the queue topic. Multiplepartitions in a topic can enable data-exchange coordinators 2506 to readfrom the queue topic in parallel.

The tasks produced by the discover coordinator(s) 2502 can bemultiplexed to the queue topic 2504. In some cases, the tasks can bemultiplexed to the queue topic 2504 via a set of logical queues. Forinstance, each logical queue may correspond with a request or job, suchas a data collection request described in association with thedata-exchange service of FIG. 19. In this regard, each task generated inassociation with a particular data collection request (e.g., via adiscover coordinator) can be obtained at a particular logical queue. Asany number of logical queues may exist, any number of logical queues canbe multiplexed to queue topic 2320. Accordingly, all tasks for all datacollection requests are collected in the queue topic 2504, for example,via multiple logical queues. Advantageously, by multiplexing multiplelogical queues to a single queue topic, multiple queue topics do notneed to be created and/or deleted.

The tasks produced via the discover coordinator(s) 2502 can be writtenor distributed to any partition of the queue topic 2504. In particular,tasks can be spread across partitions, irrespective of a particularcorresponding request or logical queue. In other words, a task is notdedicated to a particular partition (e.g., based on its association witha logical queue or request), but instead, can be provided to randompartitions. In some cases, tasks can be distributed in a random, uniformmanner.

As the tasks are spread across partitions of the queue topic 2504,multiple data-exchange coordinators 2506 can consume the tasks from thevarious partitions. As described, any number of data-exchangecoordinators 2506 may be used to consume tasks in the queue topic 2504.In some embodiments, a data-exchange coordinator 2506 may be associatedwith a particular request such that it initiates processing of taskscorresponding to the particular request. For instance, a data-exchangecoordinator 2506 may read tasks from each partition and filter toprocess tasks associated with a data collection request(s) of interest.As a specific example, a particular data-exchange coordinator 2506 canread a task and, if a task corresponds with a data collection request itis a member of (e.g., via an identifier), the data-exchange coordinator2506 can initiate processing of the task. Otherwise, tasks correspondingwith other requests can be filtered out so that the data-exchangecoordinator 2506 does not initiate processing of such messages. When adata-exchange coordinator is deployed, it can subscribe to the queuetopic 2504 to consume tasks from the queue topic.

In operation, the data-exchange coordinator 2506 initially reads a task.In cases that the processing of the task is to be initiated by thedata-exchange coordinator 2506, a start marker can be written to themarkers topic 2508. A start marker indicates the start of the taskprocessing such that monitoring the task for redelivery can begin. Inaddition to indicating the marker is a start marker (type of marker), astart marker may include any number or type of fields or attributes,such as, for example, a partition indicator (e.g., ordinal of thepartition in the queue topic that contains the task); an offset (e.g.,offset in the partition where the task is located); a redeliverydeadline (e.g., number of milliseconds to wait between when a marker isseen by the redelivery monitor and when the associated task is to beredelivered); a key (e.g., a key, for example, of a Kafka ConsumerRecord, that contains the task); a value; and/or the like.

The data-exchange coordinator 2506 can also commit the task offset suchthat the data-exchange coordinator 2506 can proceed with reading anothertask. For example, a consumer offset can be committed to the queue topic2504.

In addition to writing start markers, the data-exchange coordinator 2506can write keep-alive markers to the markers topic. For example, uponcommitting an offset to the queue topic 2504, a keep-alive marker can bewritten to the markers topic. In embodiments, for each task consumed bythe data-exchange coordinator 2506, keep-alive markers can be written orprovided to the markers topic 2508. In some cases, keep-alive markerscan be periodically produced via a background thread initiated by thedata-exchange coordinator 2506. Keep-alive markers may include anynumber and type of fields or attributes. In some cases, the keep-alivemarker may generally include the same types of fields as those includedin a start marker, with the exception of a key and value field.

In reading a task, the data-exchange coordinator 2506 can initiateprocessing of the task. For example, the data-exchange coordinator 2506may call a connector to initiate data collection via a connector toprocess a task. As described in association with FIG. 19, adata-exchange coordinator may perform load balancing of task executionbetween connectors.

Upon completion of a task processing, an end marker can be produced andprovided to the markers topic 2508 to denote that task processing hasfinished. End markers may include any number and type of fields orattributes. In some cases, an end marker may generally include the sametypes of fields as those included in a start marker, with the exceptionof a key field, value field, and redelivery deadline field, for example,to conserve storage. A keep-alive thread generated to produce (e.g.,periodically) keep-alive messages can be canceled or terminated.

The various markers can be written to partitions of the markers topic2508. In some embodiments, markers associated with a particular requestare all written, in sequential order, to a particular partition. In thisway, each partition corresponds to a particular request(s) such thatmarkers associated with a particular request(s) are not spread acrosspartitions of a markers topic. To write markers associated with aparticular request to a particular partition, a key for each marker canbe set to the request ID, or request identifier. Further, the markerscan be written in order of occurrence, such that a start marker precedesa keep-alive marker, which precedes an end marker.

In some implementations, as with the queue topic, a set of logicalqueues can be multiplexed to the markers topic 2508. As such, themarkers topic 2508 can contain markers indicating processing state forall tasks being processed. In this way, multiple logical queues can bemultiplexed on a pair of topics (e.g., queue topic and markers topic).

As described, back-pressure may be used to facilitate efficiency of thetask queueing service. Back pressure refers to a limit on a number ofmessages that are consumed. As such, back pressure can be incorporatedinto functionality of consumers. In embodiments, the number of tasksconsumed via the data-exchange coordinators 2506 is substantiallyproportional to the number of connectors managed by the data-exchangecoordinators 2506. Utilizing back pressure can limit the number of tasksbeing processed and associated marker production.

In one particular implementation in connection with Apache Kafka®, aconsumer property (e.g., max.poll.records) can be used to limit thenumber of tasks returned by a poll request. Additional consumerproperties (e.g., max.poll.interval.ms and connections.max.idle.ms) canbe used to allow for long delays between poll requests. The consumer canpoll for records, append records onto a bounded buffer (or channel), andcommit consumer offset. Tasks may be removed from the bounded buffer.The capacity of the bounded buffer (or channel) may, in embodiments, beproportional to the number of connectors managed by the coordinator(e.g., data-exchange coordinator 2506). In another implementation, areactive stream implementation may be used to ensure there isback-pressure.

Turning to the redelivery monitor 2512, the redelivery monitor 2512 isgenerally configured to manage redelivery of tasks. As described, theredelivery monitor 2512 facilitates management of redelivering messagesin the event a task is aborted due to an error (e.g., crash) occurringin a collect phase (e.g., data-exchange coordinator 2506 or connector2510 crashes) while the task is being processed. In instances in which atask is aborted, the redelivery monitor 2512 can initiate re-queueing ofthe task for execution. Any number of redelivery monitors 2512 may beused facilitate task redelivery. For example, a redelivery monitor maycorrespond with a particular number of partitions (e.g., 1, 2, 3, etc.),such that a redelivery monitor tracks markers for the correlatedpartitions. Multiple redelivery monitors 2512 may be deployed to shareconsumption of the partitions in the markers topic 2508 in order toscale redelivery.

When a redelivery monitor 2512 subscribes to the markers topic 2508, theredelivery monitor can be assigned a set of partitions from which toconsume markers. For each partition to which it is assigned, theredelivery monitor 2512 can generate and/or maintain various in-memorydata structures. Such data structures may include, for example, amarkers-in-progress mapping, a redelivery priority queue, an offsetpriority queue, and a redelivery timer. The markers-in-progress mappinggenerally tracks tasks that are currently in progress. Themarkers-in-progress mapping may include a mapping of task identity(e.g., partition, offset in the queue topic) with marker data (e.g.,key, value). The redelivery priority queue may contain tasks that areordered by redelivery deadline. The redelivery priority queue isgenerally used to determine when tasks should be redelivered to thequeue topic 2504. The redelivery priority queue may include a taskidentifier and corresponding redelivery deadline (e.g., markertimestamp+a configurable expiry time (redelivery deadline or refreshedredelivery deadline)). The offset priority queue is generally used totrack the redelivery monitor consumer offset in the partition. Theoffset priority queue may include a task identifier and marker offset,ordered by the marker offset. A redelivery timer refers to a periodicroutine to determine whether any tasks in the redelivery priority queueshould be redelivered.

Generally, the redelivery monitor 2512 performs both consumption ofmarkers and task redelivery. In performing marker consumption, theredelivery monitor 2512 consumes, or reads, markers from the markerstopic. As discussed, each task may correspond with a sequence of markersin a partition of the markers topic 2508. The redelivery monitor 2512can consume markers and perform various functions depending on theprogress state associated with the marker. In this regard, theredelivery monitor 2512 may update data structures or queues (e.g., inmemory) depending on the type of marker. As data structures may bemaintained in association with each partition, the partitioncorresponding with the marker may be initially identified to update theappropriate data structure.

For a start marker, the redelivery monitor 2512 may update themarkers-in-progress mapping, redelivery priority queue, and offsetpriority queue data structures. By way of example only, in themarkers-in-progress data structure, a mapping can be created to storethe task redelivery data for the task. For instance, partition andoffset in the queue topic can be mapped to marker data (e.g., key andvalue) such that the new task currently in progress can be tracked. Asanother example, in the redelivery priority queue, a marker entry,including partition, offset, and redelivery deadline, may be insertedinto the queue. For the offset priority queue, a marker entry, includingpartition, offset, and marker offset, may be inserted into the queue.

In accordance with reading a keep-alive marker, the redelivery monitor2512 may update the redelivery priority queue. By way of example, amarker entry's priority within the queue may be adjusted according to anupdated or extended redelivery deadline. In this regard, the redeliverymonitor 2512 may determine an updated or extended redelivery deadlinebased on reception of the keep-alive marker. The redelivery deadline maybe updated to a refreshed redelivery deadline based on the refresh time(e.g., add the refresh time to the prior redelivery deadline).

As discussed, the keep-alive marker can be used to extend or update theredelivery deadline. To do so, the redelivery deadline can be extendedby a refresh time. A refresh time may refer to an amount of time (timeduration) or a particular time to which to extend a redelivery deadline.In this regard, a refresh time may be added to a redelivery deadline toextend or delay the redelivery of a message. In some cases, a refreshtime (e.g., one hour) may be added to an initial redelivery deadline.For example, assume an initial redelivery deadline is 24 hours. Furtherassume a keep-alive marker is obtained. In such a case, the redeliverydeadline may be extended to 25 hours from the time of obtaining thestart marker. As such, if an end marker is not obtained within 25 hoursfrom obtaining the start marker, the message can be redelivered. Inother cases, a refresh time (e.g., one hour) may be added to a currentredelivery deadline. For example, assume an initial redelivery deadlineis 24 hours and two hours have passed since the redelivery deadline,leaving 22 hours until the redelivery deadline expires. Further assume akeep-alive marker is obtained. In this case, the redelivery deadline maybe extended to 23 hours from the current time. As such, if an end markeris not obtained within 23 hours from the current time, the message canbe redelivered.

In accordance with reading an end marker, the redelivery monitor 2512may update the markers-in-progress mapping, redelivery priority queue,and offset priority queue data structures. By way of example only, inthe markers-in-progress data structure, the mapping created to store thetask redelivery data for the task can be removed. As another example, inthe redelivery priority queue, the marker entry can be removed from thequeue. For the offset priority queue, the marker entry can be removedfrom the queue.

Upon updating appropriate data structures and/or queues, the redeliverymonitor 2512 can commit a smallest offset. In particular, inembodiments, the redelivery monitor 2512 can determine a smallest startmarker offset of tasks that are in progress by querying the offsetpriority queue. The smallest offset can then be committed via themarkers topic 2508.

In the event the redelivery monitor 2512 needs to rebuild its datastructure for tracking in-progress tasks (e.g., when recovering from acrash), the committed smallest offset can be used. By committing theoffset of the smallest start marker in the queue during marker topicconsumption, the redelivery monitor 2512 can rebuild the state of thepriority queue from the log at the time it crashed. In operation, theredelivery monitor 2512 can determine the end offsets in the partitionsthat it is responsible for (e.g., by calling consumer.endoffsets).Redelivery can then be performed as the redelivery monitor rebuilds itsdata structure while it consumes messages between its last committedoffset and the end offset.

As can be appreciated, when the collect phase is complete, thecorresponding data-exchange coordinator(s) may shutdown. In some cases,outstanding markers associated with the current data collection requestexecution may still be present in association with the redeliverymonitor. In such cases, the redelivery monitor may redeliver the task(s)to the queue for execution, which may be consumed by a subsequentexecution.

4.2 Efficient Message Queueing Methods

FIGS. 26-28 illustrates method of facilitating efficient messagequeueing services, in accordance with embodiments of the presentinvention. As can be appreciated, additional or alternative steps orblocks may also be included in different embodiments. Methods 2600,2700, and 2800 may be performed, for example, at an efficient messagequeuing system, such as described in FIGS. 23 and 25. The methods andordering of steps are not intended to be limited herein.

Turning initially to FIG. 26, FIG. 26 provides a method for facilitatingefficient message queuing services, in accordance with embodiments ofthe present invention. At block 2602, a coordinator, such as a discovercoordinator, provides a task to a partition of a queue topic. At block2604, the task is read by a data-exchange coordinator. The data-exchangecoordinator writes a start marker to a markers topic in a partition, asshown at block 2606. At block 2608, the data-exchange coordinatorcommits an offset. At block 2610, a keep-alive marker is written to themarkers topic to the same partition as the start marker. At block 2612,the data-exchange coordinator initiates processing of the task, forexample, via a connector. At block 2614, the data exchange coordinatormonitors the processing of the task, such that keep-alive markers arewritten (e.g., on a periodic basis) as the task remains ongoing and anend marker is written when the task completes. At block 2616, theredelivery monitor monitors the state of the task processing by readingthe markers associated with the task from the markers topic. Asdescribed herein, when keep-alive markers are read, the redeliverydeadline can be extended to allow for additional time beforeredelivering a task for reprocessing. At block 2618, the redeliverymonitor determines that a redelivery deadline or an extended redeliverydeadline has expired. Based on the expiration of the redelivery deadlineor the extended redelivery deadline, the redelivery monitor redeliversthe task to the queue topic for reprocessing of the task.

With reference to FIG. 27, FIG. 27 illustrates a method 2700 forproviding efficient message queuing services using a redelivery monitor,in accordance with embodiments of the present invention. Aspects ofmethod 2700 can be performed via a redelivery monitor, such asredelivery monitor 2340 of FIG. 23 or redelivery monitor 2512 of FIG.25. Initially, at block 2702, a marker is read from a partition of amarkers topic. Generally, the marker indicates a process state for atask processed via a component (e.g., a connector). In embodiments, themarker may be a start marker, a keep-alive marker, or an end marker. Atblock 2704, based on a determination that the marker is a keep-alivemarker indicating processing of the task is ongoing, a redeliverydeadline is extended to extend an initial amount of time or a time uponwhich to redeliver the task to a queue topic for reprocessing of thetask. At block 2706, the delivery monitor identifies that the extendedredelivery deadline has occurred, or expired. Based on the occurrence ofthe extended redelivery deadline, as shown at block 2708, redelivery ofthe task to the queue topic is initiated to reprocess of the task.

FIG. 28 provides another method 2800 for providing efficient messagequeuing services using a redelivery monitor, in accordance withembodiments of the present invention. Aspects of method 2800 can beperformed via a redelivery monitor, such as redelivery monitor 2340 ofFIG. 23 or redelivery monitor 2512 of FIG. 25. Initially, at block 2802,a marker is read. The marker generally indicates a process state for atask processed via a component (e.g., connector) and may be a startmarker, a keep-alive marker, or an end marker. At block 2804, adetermination is made as to which marker partition the marker isassociated. Based on the marker partition associated with the marker, aset of one or more data structures corresponding with the markerpartition are updated to reflect the read marker. This is shown at block2806. For example, when a start marker is read, various data structurescan be updated to reflect the start of a task, such as marker entries ina redelivery priority queue and/or offset priority queue. When akeep-alive marker is read, a marker entry's priority in the redeliverypriority queue can be updated to reflect a refreshed or extendedredelivery deadline. When an end marker is read, marker entries can beremoved from the redelivery priority queue and/or offset priority queue.At block 2808, a smallest start marker offset of tasks that are inprogress can be determined and committed.

At block 2810, a determination is made that a redelivery deadline or arefreshed redelivery deadline associated with a task has expired. Insome embodiments, such a determination can be performed periodically viaa redelivery timer thread that checks associated redelivery priorityqueue for tasks having expired redelivery deadlines, or refreshedredelivery deadlines. For the expired redelivery deadline or refreshedredelivery deadline, as indicated at block 2812, the corresponding taskis redelivered for processing.

5.0 Illustrative Hardware System

The systems and methods described above may be implemented in a numberof ways. One such implementation includes computer devices havingvarious electronic components. For example, components of the system inFIG. 25 may, individually or collectively, be implemented with deviceshaving one or more Application Specific Integrated Circuits (ASICs)adapted to perform some or all of the applicable functions in hardware.Alternatively, the functions may be performed by one or more otherprocessing units (or cores), on one or more integrated circuits orprocessors in programmed computers. In other embodiments, other types ofintegrated circuits may be used (e.g., Structured/Platform ASICs, FieldProgrammable Gate Arrays (FPGAs), and other Semi-Custom ICs), which maybe programmed in any manner known in the art. The functions of each unitmay also be implemented, in whole or in part, with instructions embodiedin a memory, formatted to be executed by one or more general orapplication-specific computer processors.

An example operating environment in which embodiments of the presentinvention may be implemented is described below in order to provide ageneral context for various aspects of the present invention. Referringto FIG. 29, an illustrative operating environment for implementingembodiments of the present invention is shown and designated generallyas computing device 2900. Computing device 2900 is but one example of asuitable operating environment and is not intended to suggest anylimitation as to the scope of use or functionality of the invention.Neither should the computing device 2900 be interpreted as having anydependency or requirement relating to any one or combination ofcomponents illustrated.

The invention may be described in the general context of computer codeor machine-useable instructions, including computer-executableinstructions such as program modules, being executed by a computer orother machine, such as a personal data assistant or other handhelddevice. Generally, program modules including routines, programs,objects, components, data structures, etc., refer to code that performparticular tasks or implement particular abstract data types. Theinvention may be practiced in a variety of system configurations,including handheld devices, consumer electronics, general-purposecomputers, more specialized computing devices, etc. The invention mayalso be practiced in distributed computing environments where tasks areperformed by remote-processing devices that are linked through acommunications network.

With reference to FIG. 29, computing device 2900 includes a bus 2910that directly or indirectly couples the following devices: memory 2912,one or more processors 2914, one or more presentation components 2916,input/output (I/O) ports 2918, I/O components 2920, and an illustrativepower supply 2922. Bus 2910 represents what may be one or more busses(such as, for example, an address bus, data bus, or combinationthereof). Although depicted in FIG. 29, for the sake of clarity, asdelineated boxes that depict groups of devices without overlap betweenthese groups of devices, in reality, this delineation is not so clearcut and a device may well fall within multiple ones of these depictedboxes. For example, one may consider a display to be one of the one ormore presentation components 2916 while also being one of the I/Ocomponents 2920. As another example, processors have memory integratedtherewith in the form of cache; however, there is no overlap depictedbetween the one or more processors 2914 and the memory 2912. A person ofskill in the art will readily recognize that such is the nature of theart, and it is reiterated that the diagram of FIG. 29 merely depicts anillustrative computing device that can be used in connection with one ormore embodiments of the present invention. It should also be noticedthat distinction is not made between such categories as “workstation,”“server,” “laptop,” “handheld device,” etc., as all such devices arecontemplated to be within the scope of computing device 2900 of FIG. 29and any other reference to “computing device,” unless the contextclearly indicates otherwise.

Computing device 2900 typically includes a variety of computer-readablemedia. Computer-readable media can be any available media that can beaccessed by computing device 2900 and includes both volatile andnonvolatile media, and removable and non-removable media. By way ofexample, and not limitation, computer-readable media may comprisecomputer storage media and communication media. Computer storage mediaincludes both volatile and nonvolatile, removable and non-removablemedia implemented in any method or technology for storage of informationsuch as computer-readable instructions, data structures, programmodules, or other data. Computer storage media includes, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which can be used to storethe desired information and which can be accessed by computing device2300. Computer storage media does not comprise signals per se, such as,for example, a carrier wave. Communication media typically embodiescomputer-readable instructions, data structures, program modules, orother data in a modulated data signal such as a carrier wave or othertransport mechanism and includes any information delivery media. Theterm “modulated data signal” means a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, communicationmedia includes wired media such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared, and otherwireless media. Combinations of any of the above should also be includedwithin the scope of computer-readable media.

Memory 2912 includes computer storage media in the form of volatileand/or nonvolatile memory. The memory may be removable, non-removable,or a combination thereof. Typical hardware devices may include, forexample, solid-state memory, hard drives, optical-disc drives, etc.Computing device 2900 includes one or more processors 2914 that readdata from various entities such as memory 2912 or I/O components 2920.Presentation component(s) 2916 present data indications to a user orother device. Illustrative presentation components include a displaydevice, speaker, printing component, vibrating component, etc.

I/O ports 2918 allow computing device 2900 to be logically coupled toother devices including I/O components 2920, some of which may be builtin. Illustrative components include a keyboard, mouse, microphone,joystick, game pad, satellite dish, scanner, printer, wireless device,etc. The I/O components 2920 may provide a natural user interface (NUI)that processes air gestures, voice, or other physiological inputsgenerated by a user. In some instances, inputs may be transmitted to anappropriate network element for further processing. An NUI may implementany combination of speech recognition, stylus recognition, facialrecognition, biometric recognition, gesture recognition both on screenand adjacent to the screen, air gestures, head and eye tracking, andtouch recognition (as described elsewhere herein) associated with adisplay of the computing device 2900. The computing device 2900 may beequipped with depth cameras, such as stereoscopic camera systems,infrared camera systems, RGB camera systems, touchscreen technology, andcombinations of these, for gesture detection and recognition.Additionally, the computing device 2900 may be equipped withaccelerometers or gyroscopes that enable detection of motion.

As can be understood, implementations of the present disclosure providefor various approaches to relating data. The present invention has beendescribed in relation to particular embodiments, which are intended inall respects to be illustrative rather than restrictive. Alternativeembodiments will become apparent to those of ordinary skill in the artto which the present invention pertains without departing from itsscope.

From the foregoing, it will be seen that this invention is one welladapted to attain all the ends and objects set forth above, togetherwith other advantages which are obvious and inherent to the system andmethod. It will be understood that certain features and subcombinationsare of utility and may be employed without reference to other featuresand subcombinations. This is contemplated by and is within the scope ofthe claims.

What is claimed is:
 1. A computer-implemented method for performing dataprocessing in a streaming manner, the computer-implemented methodcomprising: receiving a first set of tasks at a first logical queue of aplurality of logical queues, each task of the first set of tasksincluding an identifier indicating a first data collection requestassociated with the first set of tasks; receiving a second set of tasksat a second logical queue of the plurality of logical queues, each taskof the second set of tasks including an identifier indicating a seconddata collection request associated with the second set of tasks; andobtaining, at a queue topic containing tasks to be processed, the firstset of tasks associated with the first data collection request from thefirst logical queue and the second set of tasks associated with thesecond data collection request from the second logical queue, whereineach of the tasks of the first set of tasks and the second set of tasksare distributed to any partition of a plurality of partitions of thequeue topic.
 2. The method of claim 1, wherein each partition of theplurality of partitions holds a set of tasks associated with multipledata collection requests, wherein each of the tasks of the first set oftasks and the second set of tasks are assigned to one of the partitionsin accordance with a random, uniform distribution.
 3. The method ofclaim 1, wherein each of the tasks of the first set of tasks and thesecond set of tasks are distributed to any partition in a random manner.4. The method of claim 1, wherein tasks contained within variouspartitions of the plurality of partitions are read in parallel.
 5. Themethod of claim 1, wherein the plurality of partitions of the queuetopic enable tasks within the plurality of partitions to be read fromthe queue topic in parallel.
 6. The method of claim 1, wherein the firstlogical queue receives the first set of tasks associated with the firstdata collection request from a producer.
 7. The method of claim 1,wherein all tasks for all data collection requests are obtained at thequeue topic via the plurality of logical queues.
 8. The method of claim1, wherein tasks of the first set of tasks are distributed acrosspartitions of the plurality of partitions of the queue topic and tasksof the second set of tasks are distributed across partitions of theplurality of partitions.
 9. The method of claim 1, wherein a firstpartition of the plurality of partitions includes a task from the firstset of tasks and a task from the second set of tasks.
 10. The method ofclaim 1, wherein a consumer reads tasks from each partition of theplurality of partitions and filters the read tasks to process the readtasks that correspond to a data collection request of interest.
 11. Themethod of claim 1, wherein a consumer reads tasks from each partition ofthe plurality of partitions and filters the read tasks to process theread tasks that correspond to a data collection request of interest anddrop the read tasks that do not correspond to the data collectionrequest of interest.
 12. The method of claim 1 further comprisingmultiplexing a set of logical queues to the queue topic to communicatemessages associated with multiple data collection requests to the queuetopic.
 13. The method of claim 1 further comprising: reading, via aconsumer, a task from the queue topic; initiating processing of the readtask; monitoring the task processing; and writing markers indicating aprocessing state of the task to a markers topic.
 14. The method of claim1, wherein the first set of tasks and the second set of tasks areconsumed via a message queuing service that performs data processing inthe streaming manner, the message queuing service including the firstlogical queue, the second logical queue, and the queue topic.
 15. Acomputing system comprising: a processor; and computer storage memoryhaving computer-executable instructions stored thereon which, whenexecuted by the processor, configure the computing system to: receive afirst set of tasks at a first logical queue of a plurality of logicalqueues, each task of the first set of tasks including an identifierindicating a first data collection request associated with the first setof tasks; receive a second set of tasks at a second logical queue of theplurality of logical queues, each task of the second set of tasksincluding an identifier indicating a second data collection requestassociated with the second set of tasks; and obtain, at a queue topiccontaining tasks to be processed, the first set of tasks associated withthe first data collection request from the first logical queue and thesecond set of tasks associated with the second data collection requestfrom the second logical queue, wherein each of the tasks of the firstset of tasks and the second set of tasks are distributed to any onepartition of a plurality of partitions of the queue topic.
 16. One ormore computer storage media having computer-executable instructionsembodied thereon that, when executed by one or more processors, causethe one or more processors to perform a method, the method comprising:receiving a first set of tasks at a first logical queue of a pluralityof logical queues, each task of the first set of tasks including anidentifier indicating a first data collection request associated withthe first set of tasks; receiving a second set of tasks at a secondlogical queue of the plurality of logical queues, each task of thesecond set of tasks including an identifier indicating a second datacollection request associated with the second set of tasks; andobtaining, at a queue topic containing tasks to be processed, the firstset of tasks associated with the first data collection request from thefirst logical queue and the second set of tasks associated with thesecond data collection request from the second logical queue, whereineach of the tasks of the first set of tasks and the second set of tasksare distributed to any one partition of a plurality of partitions of thequeue topic.
 17. The one or more computer storage media of claim 16,wherein all tasks for all data collection requests are obtained at thequeue topic via the plurality of logical queues.
 18. The one or morecomputer storage media of claim 16, wherein tasks of the first set oftasks are distributed across partitions of the plurality of partitionsof the queue topic and tasks of the second set of tasks are distributedacross partitions of the plurality of partitions.
 19. The one or morecomputer storage media of claim 16, wherein a first partition of theplurality of partitions includes a task from the first set of tasks anda task from the second set of tasks.
 20. The one or more computerstorage media of claim 16, wherein a consumer reads tasks from eachpartition of the plurality of partitions and filters the read tasks toprocess the read tasks that correspond to a data collection request ofinterest.